Publications

2026

1. Customizing Human Machine Interfaces leveraging Digital Twins and Large Language Models

   Francesco Franco, Lorenzo Lamazzi, Marco Picone, and 3 more authors

   2026

   Abs DOI

   Mobile health applications for chronic disease management require frequent access to health data for continuous monitoring and clinical decision support, creating performance challenges when accessing centralized platforms like Google’s Health Connect (HC). This paper presents an evaluation of content provider mechanisms for optimizing health data access in Human Digital Twin applications. We developed a three-layer Human Digital Twin architecture implementing local data storage through Android’s content provider interface, integrated with TEMPO, a hemophilia management application for continuous physical activity monitoring. Our experimental evaluation compared the HDT content provider approach against direct Health Connect access over continuous monitoring periods, with automated data requests across varying request sizes from single records to large batch operations. Results demonstrate that local data access through content providers can provide performance benefits over direct API access, with implications for healthcare applications requiring frequent data access.

2. Bridging Edge and Cloud for Smart City Data and Service Continuity: The MASA Approach

   Enrico Rossini, Marcello Pietri, Marco Picone, and 3 more authors

   2026

   Abs DOI

   This paper introduces a low-cost and reproducible framework for measuring glass-to-glass (G2G) latency in real-time video systems. Unlike existing solutions, which are often proprietary, expensive, or poorly documented, our approach combines a photodiode, a microcontroller, and lightweight calibration routines to achieve accurate end-to-end latency measurements. The framework is validated across heterogeneous devices, revealing the impact of hardware tiers and video codecs (e.g., H.264 vs. Motion JPEG) on responsiveness. Beyond smartphones, we demonstrate adaptability to complex pipelines such as remote driving and wearable devices, where latency directly affects safety and user experience. Released as an open-source tool, the framework fills a methodological gap in latency research and offers practical guidelines for optimizing multimedia pipelines in domains including virtual reality, telemedicine, and autonomous mobility.

3. A Glass-to-Glass Testbed: Towards effective Latency Analysis

   Anna Semeraro, Carlo Augusto Grazia, and Luca Bedogni

   2026

   Abs DOI

   Mobile health applications integrated with wearable devices enable continuous monitoring for chronic disease management, but the reliability of wireless connectivity between mobile applications and sensors remains a concern for healthcare applications. This paper presents an analysis of Bluetooth Low Energy connection reliability between the TEMPO mobile application and Movesense wearable devices in real-world hemophilia management scenarios. We conducted a 48-hour continuous monitoring experiment simulating typical patient usage patterns, where users operate the system without active connection management. The experimental setup involved a Movesense HR paired with the TEMPO application running on an Android smartphone collecting IMU data for Human Activity Recognition applications. Our analysis demonstrates that BLE connections achieve a 90% automatic reconnection success rate, with the system effectively handling routine connectivity interruptions without requiring user intervention. The system demonstrated resilience during extended disconnection periods and successfully recovered automatically. These findings support the feasibility of BLE-based wearable systems for reliable healthcare monitoring in chronic disease management.

4. Measuring and Understanding Visualization Latency Performance for Smart City Applications

   Alessio Masola, Paolo Burgio, Carlo Augusto Grazia, and 1 more author

   2026

   Abs

   The increasing reliance on machine learning in Internet of Things systems demands to evaluate the trade off between computing on the resource constrained devices or offload the computation to more powerful edge devices. Split computing has emerged as a promising paradigm to bridge this gap by partitioning workloads between resource-constrained devices and edge devices in a flexible way. Existing approaches, however, often remain tied to specific model architectures or assume theoretical network and device conditions, hence limiting their applicability in realistic deployments. In this paper, we introduce SCIoT, a framework for Split Computing in the Internet of Things that seeks to address these shortcomings. SCIoT enables flexible and adaptive partitioning across heterogeneous devices, explicitly accounting for resource availability, fluctuating network performance, and data sensitivity. The framework incorporates dynamic policies that balance latency, bandwidth usage, and privacy, moving beyond static or one-size-fits-all strategies. We evaluate SCIoT across representative scenarios, demonstrating its ability to adaptively reconfigure computations while maintaining competitive efficiency. Our results show both the advantages and the current limitations of split computing in practice, contributing a step toward more robust, adaptive, and privacy-aware collaborative inference in IoT ecosystems.

5. Toward privacy-Aware human digital twins: A multi-Layer architecture

   L. Lamazzi, F. Franco, and L. Bedogni

   FUTURE GENERATION COMPUTER SYSTEMS, 2026

   Abs DOI

   Mobile health applications for chronic disease management require frequent access to health data for continuous monitoring and clinical decision support, creating performance challenges when accessing centralized platforms like Google’s Health Connect (HC). This paper presents an evaluation of content provider mechanisms for optimizing health data access in Human Digital Twin applications. We developed a three-layer Human Digital Twin architecture implementing local data storage through Android’s content provider interface, integrated with TEMPO, a hemophilia management application for continuous physical activity monitoring. Our experimental evaluation compared the HDT content provider approach against direct Health Connect access over continuous monitoring periods, with automated data requests across varying request sizes from single records to large batch operations. Results demonstrate that local data access through content providers can provide performance benefits over direct API access, with implications for healthcare applications requiring frequent data access.

6. Evaluating Bluetooth Low Energy Connection Reliability for Mobile Health Applications

   F. Franco, L. Lamazzi, F. Poggi, and 1 more author

   2026

   Abs DOI

   The proliferation of Internet of Things (IoT) devices has sparked a growing demand for lightweight and energy-efficient machine learning solutions, leading to the emergence of Tiny Machine Learning (TinyML). This paper presents a thorough evaluation of TinyML, encompassing its performance metrics, challenges, and prospects, focused on the use of Split Computing. Split Computing allows to offload a subset of layers of a neural network to a more powerful Edge server, to achieve a faster computation hence lower inference latency. We evaluate our proposal on a real testbed with ESP32 microcontrollers with different neural network structures, highlighting the benefits of split computing for IoT devices with varying conditions. Our results indicate that split computing on IoT devices is viable and can bring benefits particularly in heavy load scenarios where the network conditions may rapidly change.

7. SCIoT: Design and Evaluation of a Split Computing Framework for Collaborative Inference in the IoT

   L. Lamazzi, J. W. Wang, F. Franco, and 1 more author

   2026

   Abs DOI

   This paper proposes a groundbreaking architecture that reimagines Industry 5.0, emphasizing human-centric technological integration via the Web of Things (WoT) standard. Our approach innovatively digitizes human operators and machinery, creating a responsive industrial ecosystem attentive to real-time human conditions. Central to this is the Operator Thing (OT), a digital replica representing the human operator’s status and needs. This system not only recognizes operator stress and discomfort but intelligently adjusts, ensuring optimal human-machine synergy. Our methodology extends to redefining operational parameters and tasks in response to human states, balancing well-being with production efficiency. The ultimate goal is a transformative, adaptive, and empathetic Industry 5.0 environment, validated through rigorous interdisciplinary evaluation.

8. Toward Efficient Health Data Access for Mobile Applications Leveraging Human Digital Twins

   F. Franco, L. Lamazzi, F. Poggi, and 1 more author

   2026

   Abs DOI

   Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

9. Network Efficiency of Centralized and Decentralized Health Data Systems

   F. Franco, A. Bogliolo, S. Montagna, and 2 more authors

   2026

   Abs DOI

   The concept of Fluid Computing entails a dynamic resource allocation approach, enabling seamless task migration between computing nodes. This paper investigates the fusion of Fluid Computing principles with the Internet of Things (IoT) and introduces the concept of Fluid Digital Twins (FDTs) i.e. cyber-physical entities that bridge the complexities of this integration. FDTs serve as intermediaries, overseeing fluid task migration, optimizing resource use, and simplifying interactions for external digital applications. The paper delves into challenges arising from this fusion, including limited IoT device capabilities, fragmentation, and the necessity of an intelligent intermediary layer. This research article models and presents FDT mechanics, features a prototype with experimental evaluation and concludes by discussing findings and potential future research directions.

10. A navigation framework for bicycle riders based on environmental and contextual factors

    F. Montori, R. Pastore, L. Sciullo, and 2 more authors

    PERVASIVE AND MOBILE COMPUTING, 2026

    Abs DOI

    Location based services (LBS) are leveraged in everyday services and applications, as they can provide contextual and relevant information for the user needs. These services require the location of the user to be sent along with other relevant information, to provide the data in return that is relevant to the sent position. Although this opens up exciting scenarios for users, it has also been studied since it encompasses several potential privacy issues, which range from the re-identification of the user to the discovery of habits and routines. In this work, we present a study on the tradeoff between the information quality obtained from an LBS and the location precision sent by the user. Our results indicate that by sending out queries with imprecise location enhances the privacy of the users, while still providing a satisfactory quality of information.

11. A navigation framework for bicycle riders based on environmental and contextual factors.

    Federico Montori, Rocco Pastore, Luca Sciullo, and 2 more authors

    2026

    Abs DOI

    The cornerstone of Industry 5.0 is the human, its well-being, development, and creativity at the center of the production process. To achieve this, recording emotional, psychological, physical, and cognitive states efficiently in real-time is crucial. In particular, monitoring a complex psychophysiological state such as stress requires obtaining information from reliable biological signals such as electrocardiogram (ECG), galvanic skin response (GSR), electroencephalogram (EEG), and facial expressions. In this study, we introduce a new dataset, SenseCobotFusion, which collects stress-related metrics derived from physiological signals recorded from operators engaged in Human-Robot Collaboration (HRC) tasks. Labeled with the subjective operator rating obtained with the NASA-TLX questionnaire tool, SenseCobotFusion is a new dataset available to the research community focused on stress and workload detection. SenseCobotFusion is structured to be flexible and compatible with other existing datasets and potential experimental scenarios. To achieve this, a thorough dataset encompassing all metrics and specific sub-datasets for each signal type was developed, enabling seamless adaptation to user needs. As a tentative example of the potential of SenseCobotFusion, machine learning models were trained on each of these datasets. The results align with findings in the literature: the stress response is highly subjective and influenced by numerous factors, both dependent and independent of the operator. Additionally, the signal processing pipeline codes used to extract the metrics of interest, specific for GSR, EEG, ECG, and Emotions data, was also provided, which can be used as a guideline to extract stress-related metrics from SenseCobot and similar datasets.

12. Toward privacy-Aware human digital twins: A multi-Layer architecture.

    Lorenzo Lamazzi, Francesco Franco, and Luca Bedogni

    2026

    Abs DOI

    The concept of the digital twin, initially applied in industry, has recently made significant advances in healthcare, giving rise to the Human Digital Twin (HDT). This emerging technology has the potential to transform healthcare by creating virtual replicas of individuals, enabling real-time monitoring and simulation of their physiological states. HDTs integrate data from wearable sensors and other IoT devices, harnessing the power of artificial intelligence to support highly personalized healthcare services. These digital counterparts allow healthcare providers to make more informed decisions, predict health outcomes, and tailor treatments to individual needs. The development of HDTs paves the way for preventative care, chronic disease management, and continuous health monitoring representing a paradigm shift towards more proactive and patient-centered healthcare. In this paper, we propose a multi-layer architecture for Digital Twin systems that enables the seamless integration of Machine Learning (ML) and Deep Learning (DL) models. The architecture is designed to dynamically adapt to the available data sources, selecting and requesting the most appropriate model for execution based on the specific context. Our solution consists of three layers: A smartphone application that acts as a context-aware data collection platform, gathering inputs from a diverse array of sensors and querying cloud services to retrieve the most suitable ML model for the collected data. A cloud layer that serves as a repository for ML and DL models, responsible for identifying and delivering the optimal model based on the real-time sensor data and context. A fog layer, where a local node is used for data offloading, executing resource-intensive algorithms, and supporting long-term storage. This architecture achieves a balance between performance, scalability, and user privacy, providing an efficient framework for digital twin applications in personalized healthcare.

13. Customizing Human Machine Interfaces leveraging Digital Twins and Large Language Models.

    Francesco Franco, Lorenzo Lamazzi, Marco Picone 0001, and 3 more authors

    2026

    Abs DOI

    Crowd-sensing is considered a robust model for data collection, yet with challenges related to data availability and privacy. Traditional techniques such as data encryption and anonymization may not fully mitigate these issues, since anonymized data can still be traced back to individual users, and the volume of data generated can reveal user identities. This paper introduces a system that employs smart contracts and blockchain technology to manage crowd-sensing campaigns. The smart contract oversees user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive mechanisms within the smart contract promote user participation. Simulation results validate the system’s feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.

14. Measuring and Understanding Visualization Latency Performance for Smart City Applications.

    Alessio Masola, Paolo Burgio, Carlo Augusto Grazia, and 1 more author

    2026

    Abs DOI

    Crowdsensing platforms face a fundamental trade-off between data utility and participant privacy, where traditional approaches require users to expose sensitive identity information, creating barriers to widespread adoption. This paper presents a blockchain- based protocol that addresses these privacy concerns through pseudonymous participation and secure data exchange mechanisms. Our approach exploits smart contracts as trusted intermediaries to eliminate di-rect communication between data initiators and contributors, while employing asymmetric cryptography to enable secure key exchange without pre-established channels. The protocol enhances privacy through campaign-specific key pairs that remain unlinkable to participants’ persistent identities, contex-tual separation of cryptographic identities, and anonymization sets that obscure individual actions within larger groups. We developed a prototype implementation to verify the correctness of the protocol and to evaluate gas consumption. Through simulation, we assess system performance under varying user dynamics and activity rates. Results confirm the viability of the proposal.

15. The Role of Personalization Weights in Sensor-Based Human Activity Recognition

    Emiliano Chiarini, Angelo Ferrando, and Luca Bedogni

    In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom 2026 - Workshops, Pisa, Italy, March 16-20, 2026, 2026

    Abs DOI

    Personalising Human Activity Recognition (HAR) models remains a major challenge due to inter-subject variability, commonly referred to as user-induced drift. While few-shot personalization and transfer learning are widely adopted, existing approaches typically assume that all personalized samples contribute equally to the model. In this work, we study an overlooked dimension of personalization: the importance assigned to the new user’s data. We systematically analyse how different importance weights affect performance when only a small amount of labelled data from the target user is available. Using two public datasets with distinct sensor configurations (SelfBACK and SDALLE), we evaluate personalization under a Leave-One-User-Out Cross-Validation protocol and implement our models using XG

16. Privacy-Preserving Multi-Layer Human Digital Twins with User-Controlled Data Management

    Francesco Franco, Lorenzo Lamazzi, Emanuele Zarfati, and 1 more author

    In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom 2026 - Workshops, Pisa, Italy, March 16-20, 2026, 2026

    Abs DOI

    Human Digital Twin (HDT) enables personalized monitoring and support in various domains by continuously collecting data from wearable sensors and mobile devices. We present a privacy preserving multi-layer HDT architecture that integrates privacy by design principles with user controlled data management. Our architecture distributes processing across three layers: an Edge layer for local data collection and inference, a Fog layer for complex computations, and a Cloud layer serving as a model repository. Users maintain granular control over sensor activation, data collection, and processing location through a dynamic model selection interface that implements privacy aware filtering. We validate our architecture through three Human Activity Recognition scenarios representing different use ca

2025

1. Decentralized Health Data Management: An IPFS-based Approach and Performance Evaluation

   F. Franco, A. Bogliolo, S. Montagna, and 2 more authors

   2025

   Abs DOI

   Current health data management relies on centralized architectures that create a single point of failure, limit patient autonomy, and increase vulnerability to data breaches and vendor lock-in. This paper presents a decentralized approach to continuous health monitoring through the integration of wearable devices and distributed file systems.

2. SenseCobotFusion Dataset: Unlocking new avenues for stress detection in Industry 5.0

   Simone Borghi, Alberto Nuzzaci, Margherita Peruzzini, and 2 more authors

   2025

   Abs DOI

   The concept of the digital twin, initially applied in industry, has recently made significant advances in healthcare, giving rise to the Human Digital Twin (HDT). This emerging technology has the potential to transform healthcare by creating virtual replicas of individuals, enabling real-time monitoring and simulation of their physiological states. HDTs integrate data from wearable sensors and other IoT devices, harnessing the power of artificial intelligence to support highly personalized healthcare services. These digital counterparts allow healthcare providers to make more informed decisions, predict health outcomes, and tailor treatments to individual needs. The development of HDTs paves the way for preventative care, chronic disease management, and continuous health monitoring representing a paradigm shift towards more proactive and patient-centered healthcare. In this paper, we propose a multi-layer architecture for Digital Twin systems that enables the seamless integration of Machine Learning (ML) and Deep Learning (DL) models. The architecture is designed to dynamically adapt to the available data sources, selecting and requesting the most appropriate model for execution based on the specific context. Our solution consists of three layers: A smartphone application that acts as a context-aware data collection platform, gathering inputs from a diverse array of sensors and querying cloud services to retrieve the most suitable ML model for the collected data. A cloud layer that serves as a repository for ML and DL models, responsible for identifying and delivering the optimal model based on the real-time sensor data and context. A fog layer, where a local node is used for data offloading, executing resource-intensive algorithms, and supporting long-term storage. This architecture achieves a balance between performance, scalability, and user privacy, providing an efficient framework for digital twin applications in personalized healthcare.

3. Intelligent Healthcare Navigation: Personalized Path Planning with Patient Condition and Environmental Awareness

   R. Apriyanti, S. Montagna, and L. Bedogni

   2025

   Abs DOI

   Traditional pedestrian navigation systems optimize only for travel time or distance, ignoring environmental factors that significantly impact individuals with chronic health conditions. We propose a novel approach to urban navigation that incorporates healthcare considerations and environmental factors into route planning.

4. Efficient and Flexibile IoT Communication Through a Plugin-Based MQTT Processing Architecture

   M. Pietri, L. Taccini, L. Bedogni, and 3 more authors

   2025

   Abs DOI

   The study explores advanced MQTT message handling—filtering, modifying, and rerouting messages in IoT networks. We propose two solutions: modifying the MQTT broker or deploying an external module to process and re-publish messages, enhancing network flexibility and reducing traffic.

5. Smart Contract Coordinated Privacy Preserving Crowd-Sensing Campaigns

   L. Bedogni, and S. Ferretti

   2025

   Abs DOI

   This research paper proposes a decentralized framework for managing crowd-sensing campaigns using blockchain and smart contracts. The system aims to solve the critical tension between data availability and user privacy, specifically addressing the vulnerabilities of traditional anonymization in sparsely populated areas.

6. A Multi-Layer architecture for Human Digital Twin

   F. Franco, L. Lamazzi, and L. Bedogni

   2025

   Abs DOI

   Crowd-sensing is considered a robust model for data collection, yet with challenges related to data availability and privacy. Traditional techniques such as data encryption and anonymization may not fully mitigate these issues, since anonymized data can still be traced back to individual users, and the volume of data generated can reveal user identities. This paper introduces a system that employs smart contracts and blockchain technology to manage crowd-sensing campaigns. The smart contract oversees user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive mechanisms within the smart contract promote user participation. Simulation results validate the system’s feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.

7. Incentivizing Decentralized Privacy-Preserving Crowd-Sensing with Smart Contracts

   L. Bedogni, and S. Ferretti

   2025

   Abs DOI

   Crowdsensing platforms face a fundamental trade-off between data utility and participant privacy, where traditional approaches require users to expose sensitive identity information, creating barriers to widespread adoption. This paper presents a blockchain- based protocol that addresses these privacy concerns through pseudonymous participation and secure data exchange mechanisms. Our approach exploits smart contracts as trusted intermediaries to eliminate di-rect communication between data initiators and contributors, while employing asymmetric cryptography to enable secure key exchange without pre-established channels. The protocol enhances privacy through campaign-specific key pairs that remain unlinkable to participants’ persistent identities, contex-tual separation of cryptographic identities, and anonymization sets that obscure individual actions within larger groups. We developed a prototype implementation to verify the correctness of the protocol and to evaluate gas consumption. Through simulation, we assess system performance under varying user dynamics and activity rates. Results confirm the viability of the proposal.

8. A Web of Things approach for learning on the Edge–Cloud Continuum

   L. Bedogni, and F. Chiariotti

   FUTURE GENERATION COMPUTER SYSTEMS, 2025

   Abs DOI

   In this work, we investigate the performance and suitability of various mobile frameworks for critical smart city applications. With the increasing reliance on mobile apps for urban services, understanding the capabilities and assessing the limitations of different development platforms is crucial. We evaluate Android OS, Apple iOS, and cross-platform solutions like Flutter, focusing on their ability to deliver real-time information efficiently in mobile applications tailored for smart city environments. In our work, we look at the content delivery latency, scalability, cross-platform capabilities, and developer productivity. We also assess the frameworks’ integration with existing smart city infrastructure and their handling of distributed location-dependent data. The study compares native implementations using platform-specific SDKs against cross-platform solutions, with particular attention to map-based applications utilizing both native maps and third-party services like Mapbox. We have tested the platforms within the Modena Automotive Smart Area (MASA), a real-world testbed for innovative mobility solutions in Modena, Italy. This environment provided a realistic setting to evaluate the frameworks’ performance in critical smart city scenarios. The outcomes of our work offer insights into the relative strengths and limitations of each mobile framework, considering both technical performance and development efficiency. In this work, we aim to guide developers and city planners in selecting the most appropriate mobile framework for smart city applications, balancing performance requirements with development resources and cross-platform needs.

9. Towards Anonymous Crowdsensing: A Smart Contract-Mediated Privacy Framework

   G. Cacciapuoti, C. A. Cartarasa, D. Cavalca, and 2 more authors

   2025

   Abs DOI

   This study investigates the impact of meteorological variables on air pollutant concentrations, focusing on Carbon Monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3). By integrating data from static stations and other services, alongside weather data from online public repositories, we aim to enhance the understanding of air quality dynamics. The research highlights how temperature and solar radiation significantly influence air quality, with wind speed and precipitation aiding in pollutant dispersion. Utilizing the SHAP method, we offer a detailed and interpretable analysis of the factors affecting air quality, emphasizing the crucial role of integrating diverse data sources. Our findings demonstrate that merging various datasets fills critical gaps in environmental monitoring, leading to improved interpretability and reliability in air quality assessments. These insights support more effective environmental management strategies. Future directions include leveraging citizen-generated data to refine pollution modeling and enhance forecast transparency, ultimately contributing to more comprehensive environmental monitoring practices.

10. Fluid Computing & Digital Twins for intelligent interoperability in the IoT ecosystem

    Luca Bedogni, Marco Mamei, Marco Picone, and 2 more authors

    FUTURE GENERATION COMPUTER SYSTEMS, 2025

    Abs DOI

    This paper presents the Smart City Architecture (SCA), a middleware system built upon the MQTT (Message Queuing Telemetry Transport) protocol and developed within the MASA (Modena Automotive Smart Area) initiative. SCA enables intelligent urban applications by facilitating seamless and scalable communication among heterogeneous entities, including assets, services, and observers. Its structured, topic-based messaging layer supports efficient telemetry exchange, event-driven processing, and dynamic service interaction. The capabilities of SCA are exemplified through two real-world services—Vulnerable Road User (VRU) and GeoPerception—which provide real-time risk detection and localized situational awareness in smart city scenarios.

11. On the Latency Performance of Mobile Mapping Services: Towards Vulnerable Road Users Safety

    L. Bedogni, C. A. Grazia, and R. Scalise

    2025

    Abs DOI

    Mobile health applications integrated with wearable devices enable continuous monitoring for chronic disease management, but the reliability of wireless connectivity between mobile applications and sensors remains a concern for healthcare applications. This paper presents an analysis of Bluetooth Low Energy connection reliability between the TEMPO mobile application and Movesense wearable devices in real-world hemophilia management scenarios. We conducted a 48-hour continuous monitoring experiment simulating typical patient usage patterns, where users operate the system without active connection management. The experimental setup involved a Movesense HR paired with the TEMPO application running on an Android smartphone collecting IMU data for Human Activity Recognition applications. Our analysis demonstrates that BLE connections achieve a 90% automatic reconnection success rate, with the system effectively handling routine connectivity interruptions without requiring user intervention. The system demonstrated resilience during extended disconnection periods and successfully recovered automatically. These findings support the feasibility of BLE-based wearable systems for reliable healthcare monitoring in chronic disease management.

12. Assessing Benefits and Limitations of Multiple Data Sources for Environmental Monitoring

    R. A. Purba, and L. Bedogni

    2025

    Abs DOI

    The increasing reliance on machine learning in Internet of Things systems demands to evaluate the trade off between computing on the resource constrained devices or offload the computation to more powerful edge devices. Split computing has emerged as a promising paradigm to bridge this gap by partitioning workloads between resource-constrained devices and edge devices in a flexible way. Existing approaches, however, often remain tied to specific model architectures or assume theoretical network and device conditions, hence limiting their applicability in realistic deployments. In this paper, we introduce SCIoT, a framework for Split Computing in the Internet of Things that seeks to address these shortcomings. SCIoT enables flexible and adaptive partitioning across heterogeneous devices, explicitly accounting for resource availability, fluctuating network performance, and data sensitivity. The framework incorporates dynamic policies that balance latency, bandwidth usage, and privacy, moving beyond static or one-size-fits-all strategies. We evaluate SCIoT across representative scenarios, demonstrating its ability to adaptively reconfigure computations while maintaining competitive efficiency. Our results show both the advantages and the current limitations of split computing in practice, contributing a step toward more robust, adaptive, and privacy-aware collaborative inference in IoT ecosystems.

13. A Web of Things approach for learning on the Edge-Cloud Continuum.

    Luca Bedogni, and Federico Chiariotti

    2025

    Abs DOI

    In this work, we investigate the performance and suitability of various mobile frameworks for critical smart city applications. With the increasing reliance on mobile apps for urban services, understanding the capabilities and assessing the limitations of different development platforms is crucial. We evaluate Android OS, Apple iOS, and cross-platform solutions like Flutter, focusing on their ability to deliver real-time information efficiently in mobile applications tailored for smart city environments. In our work, we look at the content delivery latency, scalability, cross-platform capabilities, and developer productivity. We also assess the frameworks’ integration with existing smart city infrastructure and their handling of distributed location-dependent data. The study compares native implementations using platform-specific SDKs against cross-platform solutions, with particular attention to map-based applications utilizing both native maps and third-party services like Mapbox. We have tested the platforms within the Modena Automotive Smart Area (MASA), a real-world testbed for innovative mobility solutions in Modena, Italy. This environment provided a realistic setting to evaluate the frameworks’ performance in critical smart city scenarios. The outcomes of our work offer insights into the relative strengths and limitations of each mobile framework, considering both technical performance and development efficiency. In this work, we aim to guide developers and city planners in selecting the most appropriate mobile framework for smart city applications, balancing performance requirements with development resources and cross-platform needs.

14. Fluid Computing & Digital Twins for intelligent interoperability in the IoT ecosystem.

    Luca Bedogni, Marco Mamei, Marco Picone 0001, and 2 more authors

    2025

    Abs DOI

    This study investigates the impact of meteorological variables on air pollutant concentrations, focusing on Carbon Monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3). By integrating data from static stations and other services, alongside weather data from online public repositories, we aim to enhance the understanding of air quality dynamics. The research highlights how temperature and solar radiation significantly influence air quality, with wind speed and precipitation aiding in pollutant dispersion. Utilizing the SHAP method, we offer a detailed and interpretable analysis of the factors affecting air quality, emphasizing the crucial role of integrating diverse data sources. Our findings demonstrate that merging various datasets fills critical gaps in environmental monitoring, leading to improved interpretability and reliability in air quality assessments. These insights support more effective environmental management strategies. Future directions include leveraging citizen-generated data to refine pollution modeling and enhance forecast transparency, ultimately contributing to more comprehensive environmental monitoring practices.

15. Human in the Loop in Digital Twins Enabled Active Learning: A Proposed Architecture.

    Lorenzo Lamazzi, Francesco Franco, Riccardo Morandi, and 2 more authors

    2025

    Abs DOI

    This paper presents the Smart City Architecture (SCA), a middleware system built upon the MQTT (Message Queuing Telemetry Transport) protocol and developed within the MASA (Modena Automotive Smart Area) initiative. SCA enables intelligent urban applications by facilitating seamless and scalable communication among heterogeneous entities, including assets, services, and observers. Its structured, topic-based messaging layer supports efficient telemetry exchange, event-driven processing, and dynamic service interaction. The capabilities of SCA are exemplified through two real-world services—Vulnerable Road User (VRU) and GeoPerception—which provide real-time risk detection and localized situational awareness in smart city scenarios.

16. SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0.

    Simone Borghi, Alberto Nuzzaci, Margherita Peruzzini, and 2 more authors

    2025

    Abs DOI

    This paper introduces a low-cost and reproducible framework for measuring glass-to-glass (G2G) latency in real-time video systems. Unlike existing solutions, which are often proprietary, expensive, or poorly documented, our approach combines a photodiode, a microcontroller, and lightweight calibration routines to achieve accurate end-to-end latency measurements. The framework is validated across heterogeneous devices, revealing the impact of hardware tiers and video codecs (e.g., H.264 vs. Motion JPEG) on responsiveness. Beyond smartphones, we demonstrate adaptability to complex pipelines such as remote driving and wearable devices, where latency directly affects safety and user experience. Released as an open-source tool, the framework fills a methodological gap in latency research and offers practical guidelines for optimizing multimedia pipelines in domains including virtual reality, telemedicine, and autonomous mobility.

17. A Multi-Layer architecture for Human Digital Twin.

    Francesco Franco, Lorenzo Lamazzi, and Luca Bedogni

    2025

    Abs DOI

    Mobile health applications integrated with wearable devices enable continuous monitoring for chronic disease management, but the reliability of wireless connectivity between mobile applications and sensors remains a concern for healthcare applications. This paper presents an analysis of Bluetooth Low Energy connection reliability between the TEMPO mobile application and Movesense wearable devices in real-world hemophilia management scenarios. We conducted a 48-hour continuous monitoring experiment simulating typical patient usage patterns, where users operate the system without active connection management. The experimental setup involved a Movesense HR paired with the TEMPO application running on an Android smartphone collecting IMU data for Human Activity Recognition applications. Our analysis demonstrates that BLE connections achieve a 90% automatic reconnection success rate, with the system effectively handling routine connectivity interruptions without requiring user intervention. The system demonstrated resilience during extended disconnection periods and successfully recovered automatically. These findings support the feasibility of BLE-based wearable systems for reliable healthcare monitoring in chronic disease management.

18. Dynamic Machine Learning Models Management for Operator Digital Twins in Industry 5.0.

    Lorenzo Lamazzi, Francesco Franco, Luca Bedogni, and 1 more author

    2025

    Abs DOI

    The adoption of decentralized architectures for health data management offers benefits including patient data sovereignty and elimination of single points of failure, but introduces questions about network overhead compared to traditional centralized systems. This paper presents a network overhead analysis comparing Firebase Real-Time Database with IPFS-based storage via Pinata for mobile health data transmission. We implemented an Android application that collects physiological data from wearable devices and transmits this information to both backends using REST APIs. Our experimental evaluation across eight transmission scales reveals that Firebase demonstrates lower fixed overhead and latency for small payloads, while Pinata exhibits superior scaling characteristics for larger data volumes. A crossover point occurs around 50 records per payload, beyond which the decentralized architecture transmits less total data than the centralized alternative. The results indicate that neither architecture maintains uniform efficiency across all operational scales, with architectural choice depending on expected transaction patterns in the deployment context.

2024

1. Performance Evaluation of Split Computing with TinyML on IoT Devices

   Fabio Bove, Simone Colli, and Luca Bedogni

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   Nowadays, inertial sensors are embedded in almost every smartphone and are a key enabler for a wide variety of applications that build on motion. However, in the context of major mobile operating systems, these sensors do not require any permission to be used. This may cause privacy and security breaches, as motion sensors can infer a multitude of derived conditions. Our paper aims to bring attention to keylogging through inertial sensors, in which they are used to understand what the user is typing building on how the device moves or tilts. We propose a pipeline for detecting whole words by applying a combination of supervised and unsupervised methods, to identify portions of the keyboards that display similar sensor values. We then combine this method further with word frequencies in a corpus to improve the detection accuracy. We performed a data gathering campaign by distributing a mobile app to multiple users and built up a real world dataset which we used to evaluate our proposal.

2. Towards Coordinating Machines and Operators in Industry 5.0 through the Web of Things

   Marco Picone, Valeria Villani, Marcello Pietri, and 1 more author

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   This paper proposes a groundbreaking architecture that reimagines Industry 5.0, emphasizing human-centric technological integration via the Web of Things (WoT) standard. Our approach innovatively digitizes human operators and machinery, creating a responsive industrial ecosystem attentive to real-time human conditions. Central to this is the Operator Thing (OT), a digital replica representing the human operator’s status and needs. This system not only recognizes operator stress and discomfort but intelligently adjusts, ensuring optimal human-machine synergy. Our methodology extends to redefining operational parameters and tasks in response to human states, balancing well-being with production efficiency. The ultimate goal is a transformative, adaptive, and empathetic Industry 5.0 environment, validated through rigorous interdisciplinary evaluation.

3. Wi-Fi Sensing for Human Identification Through ESP32 Devices: An Experimental Study

   Fabio Gaiba, Luca Bedogni, Giacomo Gori, and 2 more authors

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   Recent studies explore the possibility of detecting events in a room via Wi-Fi Sensing. This practice exploits the interaction between waves carrying Wi-Fi signals and the elements present in an environment. These interactions are called Channel State Information (CSI) and can be analyzed and exploited to infer information about the environment, such as “device-free” Human Activity Recognition, Human Identification, and more. Considering identification, we recently saw an increasing trend in the usage of low-end devices such as ESP32. Being small and low-power, they are cheap and versatile, however, the quality of the collected data is inferior. In this work, we use state-of-the-art tools to perform Human Identification using the ESP32. Software is created to act as an interface between the collected data and the algorithms suitable for Wi-Fi Sensing. To evaluate the final design, we performed a data collection in a controlled environment. The experiments show an accuracy of 95% in distinguishing two users while 74% in distinzuishing three.

4. On the Decentralization of Mobile Crowdsensing in Distributed Ledgers: An Architectural Vision

   Lorenzo Gigli, Federico Montori, Mirko Zichichi, and 3 more authors

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

5. Fluid Computing in the Internet of Things: A Digital Twin Approach

   Luca Bedogni, Marco Picone, Marcello Pietri, and 2 more authors

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   The concept of Fluid Computing entails a dynamic resource allocation approach, enabling seamless task migration between computing nodes. This paper investigates the fusion of Fluid Computing principles with the Internet of Things (IoT) and introduces the concept of Fluid Digital Twins (FDTs) i.e. cyber-physical entities that bridge the complexities of this integration. FDTs serve as intermediaries, overseeing fluid task migration, optimizing resource use, and simplifying interactions for external digital applications. The paper delves into challenges arising from this fusion, including limited IoT device capabilities, fragmentation, and the necessity of an intelligent intermediary layer. This research article models and presents FDT mechanics, features a prototype with experimental evaluation and concludes by discussing findings and potential future research directions.

6. On the Trade-Off Between Privacy and Information Quality in Location Based Services

   Francesco Apollonio, Luca Bedogni, Giacomo Gori, and 2 more authors

   In 21st IEEE Consumer Communications & Networking Conference, CCNC 2024, Las Vegas, NV, USA, January 6-9, 2024, 2024

   Abs DOI

   Location based services (LBS) are leveraged in everyday services and applications, as they can provide contextual and relevant information for the user needs. These services require the location of the user to be sent along with other relevant information, to provide the data in return that is relevant to the sent position. Although this opens up exciting scenarios for users, it has also been studied since it encompasses several potential privacy issues, which range from the re-identification of the user to the discovery of habits and routines. In this work, we present a study on the tradeoff between the information quality obtained from an LBS and the location precision sent by the user. Our results indicate that by sending out queries with imprecise location enhances the privacy of the users, while still providing a satisfactory quality of information.

7. Raising Awareness for Inertial Sensors-based Keylogging on Smartphones

   Federico Montori, Luca Sciullo, and Luca Bedogni

   In Proceedings of the 2024 International Conference on Information Technology for Social Good, GoodIT 2024, Bremen, Germany, September 4-6, 2024, 2024

   Abs DOI

   In the fast-paced urban landscapes of today, the demand for advanced route planning solutions that cater to personalized navigation and environmental consciousness is more pressing than ever. In this work we present the Urban Route Planner, a state-of-the-art framework poised to revolutionize urban navigation by providing tailored route recommendations that not only consider individual preferences but also prioritize environmental factors. Our work delves into the conceptual framework and essential design elements of our proposal, high-lighting its potential to redefine urban navigation by delivering personalized route recommendations that promote environmental awareness and user satisfaction. With a focus on enhancing both the functionality and sustainability of urban transportation, this framework represents a significant step towards a more intelligent and user-centric approach to city navigation. Our results on different cities highlight the viability of our approach, and pave the way for future contributions to this field.

8. Smart Path Planner: Enhancing Personalized Navigation and Environmental Awareness

   Rini Apriyanti Purba, Neri Riccardo, and Luca Bedogni

   In IEEE/ACM Symposium on Edge Computing, SEC 2024, Rome, Italy, December 4-7, 2024, 2024

   Abs DOI

   In the era of ubiquitous computing, the collection of users’ geographical location is increasingly widespread. This represents an enabling technology, capable of creating new type of services but at the same time represents a new digital asset that needs to be protected in order to safeguard the users’ privacy. In fact, exploiting everyday movements, it is possible for a threat actor to gather sensible information about the victims that can be leveraged afterwards. In this preliminary paper, we reproduced some major results in the field of re-identification of users’ trajectories, validating them under scenarios where different countermeasures for geographical data are in place. Specifically, we tested generalization of spatial data using geohashing and K-means clustering. The results were obtained using a dataset that collects users from all over the world, allowing the clustering methods to range on very different scales. Results shows that, even if a strong data generalization is applied, users’ trajectories keep their uniqueness, showing high re-identification ratios. Nevertheless, the usability issues typical of these techniques are still present, having only few tens of points for covering the entire globe which cannot be considered a general solution for every possible use case of such data.

9. Effects of Geohashing and K-Means Clustering on Uniqueness in a Mobility Dataset

   Andrea Artioli, Luca Bedogni, and Mauro Andreolini

   In IEEE/ACM Symposium on Edge Computing, SEC 2024, Rome, Italy, December 4-7, 2024, 2024

   Abs DOI

   The rapid advancement of Internet of Things (IoT) devices requires innovative approaches to implement machine learning (ML) in resource-constrained environments. This paper explores the integration of Tiny Machine Learning (TinyML) with split computing, focusing on classification using an ESP32 microcontroller connected to a Raspberry Pi edge server. We conduct a series of experiments to measure the time required for image capture and classification, rather than focusing solely on model accuracy. Our findings indicate that while local processing on the ESP32 is limited by its computational capabilities, the split computing approach significantly reduces the processing time by leveraging the Raspberry Pi’s computing resources. We then highlight the benefits of our approach considering a dynamic scenario, in which networking changes hence the possibilities to perform split computing vary over time.

10. Smart Split: Leveraging TinyML and Split Computing for Efficient Edge AI

    Fabio Bove, and Luca Bedogni

    In IEEE/ACM Symposium on Edge Computing, SEC 2024, Rome, Italy, December 4-7, 2024, 2024

    Abs DOI

    The rise of wearable devices offers numerous opportunities for monitoring human activities and behaviors, even outside hospital settings. Human Activity Recognition techniques utilize sensor data from wearables and smartphones to extract patterns and determine the activities performed. This study focuses on Human Activity Recognition for Haemophilia patients, to identify the optimal sensor positions for accurate activity detection. We have collected data from 5 key activities using multiple wearable devices, to determine the most informative features and device positions. Our results indicate that placing the devices on the ankle, closer to the source of movements, achieves the highest performance. Using such device, we are able to recognize these activities with F1 scores close to 1.

11. On the Limits of Digital Twins for Safe Deep Reinforcement Learning in Robotic Networks

    Mohamed Iheb Balghouthi, Federico Chiariotti, and Luca Bedogni

    In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops, Vancouver, BC, Canada, May 20, 2024, 2024

    Abs DOI

    The concept of Industry 5.0 is set to revolutionize the landscape of modern manufacturing, emphasizing human-centricity and elevating the well-being of industry workers as a central tenet of the production process. This paper extends this vision by integrating the dimension of health, focusing not only on the well-being of the operator but also on the detection of their health condition, predicting potential issues, and consequently enhancing their overall welfare. Building upon this enhanced perspective, our work explores the role of Operator Digital Twins (ODTs), which are instrumental in creating a symbiotic relationship between human operators and industrial machinery. ODTs act as digital counterparts, reflecting the physical and cognitive states of operators, thus facilitating real-time monitoring of their capabilities, workload, stress levels, and various health-related parameters. The paper delves into the motivations driving the development of ODTs, abstractly models their functions, and outlines the architectural blueprint. We present an initial ODT prototype with wearable technology and simulated data together with a discussion of the experimental insights and outcomes.

12. Wearable Device Positioning for Activity Recognition and Monitoring

    Andrea Montanari, Alexandra Marele, Francesco Franco, and 2 more authors

    In IEEE Symposium on Computers and Communications, ISCC 2024, Paris, France, June 26-29, 2024, 2024

    Abs DOI

    This work explores the integration and experimental evaluation of Fluid Computing principles with the Internet of Things (IoT) through the concept of Fluid Digital Twins (FDTs). They have been recently introduced as a cyberphysical paradigm designed to serve as intermediate software components aiming to enable seamless task migration, optimize resource utilization, and streamline interactions. Expanding upon this investigation, the research investigates FDTs within the context of the edge-to-cloud compute continuum. It models and explores the feasibility and ramifications of deploying and orchestrating FDTs and their dynamic capabilities across diverse computational facilities, from edge devices to cloud infrastructure. The paper outlines a new distributed FDT’s modeling, presents the implemented prototype within a target reference use case together with its experimental evaluations, and analyzes challenges and opportunities inherent in this dynamic integration.

13. Towards Operator Digital Twins in Industry 5.0: Design Strategies & Experimental Evaluation

    Marco Picone, Riccardo Morandi, Valeria Villani, and 2 more authors

    In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom 2024 - Workshops, Biarritz, France, March 11-15, 2024, 2024

    Abs DOI

    Road surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results.

14. Digital Twins & Fluid Computing in the Edge-to-Cloud Compute Continuum

    Marco Picone, Luca Bedogni, Marcello Pietri, and 2 more authors

    In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom 2024 - Workshops, Biarritz, France, March 11-15, 2024, 2024

    Abs DOI

    In recent years, there has been a growth in the development of numerous software algorithms dedicated to pedometers (or step counters). This surge has subsequently spurred the creation of various context-aware smartphone applications for sports, healthcare, and other fields. Most works that compare commercial offerings do not adopt a sound and rigorous method, as human testers are asked to stick to a defined set of constraints, and experiments are carried out within controlled environments. However, each application is still tested separately, with no guarantee that the conditions are really the same, plus these conditions cannot resemble the real environment where pedometers are going to be used. Our proposal features a software solution that records the sensor readings of human testers and inject the exact same sensor values into different pedometer applications to produce a sound result by using the same testing conditions. We implement our solution and perform with it a comparison study.

15. An MCS Navigation System Based on Road Surface Quality for Bicycle Riders

    Federico Montori, Rocco Pastore, Luca Sciullo, and 2 more authors

    In IEEE International Conference on Smart Computing, SMARTCOMP 2024, Osaka, Japan, June 29 - July 2, 2024, 2024

    Abs DOI

    Road surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results.

16. Comparison of Commercial Pedometer Applications: A Rigorous Approach

    Alessio Terzi, Federico Montori, Lorenzo Gigli, and 3 more authors

    In IEEE International Conference on Smart Computing, SMARTCOMP 2024, Osaka, Japan, June 29 - July 2, 2024, 2024

    Abs DOI

    In recent years, there has been a growth in the development of numerous software algorithms dedicated to pedometers (or step counters). This surge has subsequently spurred the creation of various context-aware smartphone applications for sports, healthcare, and other fields. Most works that compare commercial offerings do not adopt a sound and rigorous method, as human testers are asked to stick to a defined set of constraints, and experiments are carried out within controlled environments. However, each application is still tested separately, with no guarantee that the conditions are really the same, plus these conditions cannot resemble the real environment where pedometers are going to be used. Our proposal features a software solution that records the sensor readings of human testers and inject the exact same sensor values into different pedometer applications to produce a sound result by using the same testing conditions. We implement our solution and perform with it a comparison study.

17. Smart Contract Coordinated Privacy Preserving Crowd-Sensing Campaigns

    Luca Bedogni, and Stefano Ferretti

    CoRR, 2024

    Abs DOI

    Mobile crowdsensing (MCS) is a valuable approach for data collection via personal devices, however, it faces challenges in data security and reward to end users. Blockchain is considered a viable addition to MCS, though few real integrations of this kind are deployed. In this paper, we explore the integration of blockchain into MCS, highlighting benefits and architectural adaptation challenges. We then present a novel mobile distributed application (MDapp) that serves crowdsourcers, workers, and verifiers in managing campaigns, data contribution and validation, as well as in the reward process. We also provide a quantitative analysis across different blockchains to highlight the feasibility and economic considerations of this integration.

18. Dynamic Function Validation and Simulation in Fluid Digital Twins.

    Marco Picone 0001, Luca Bedogni, Marcello Pietri, and 2 more authors

    2024

    Abs DOI

    The proliferation of Internet of Things (IoT) devices has sparked a growing demand for lightweight and energy-efficient machine learning solutions, leading to the emergence of Tiny Machine Learning (TinyML). This paper presents a thorough evaluation of TinyML, encompassing its performance metrics, challenges, and prospects, focused on the use of Split Computing. Split Computing allows to offload a subset of layers of a neural network to a more powerful Edge server, to achieve a faster computation hence lower inference latency. We evaluate our proposal on a real testbed with ESP32 microcontrollers with different neural network structures, highlighting the benefits of split computing for IoT devices with varying conditions. Our results indicate that split computing on IoT devices is viable and can bring benefits particularly in heavy load scenarios where the network conditions may rapidly change.

2023

1. Joint privacy and data quality aware reward in opportunistic Mobile Crowdsensing systems

   Luca Bedogni, and Federico Montori

   J. Netw. Comput. Appl., 2023

   Abs DOI

   Pervasive devices are now part of daily lives for a multitude of human beings, due to their ability to perform simple to more complex tasks. Scenarios like Industry 4.0 and drone delivery are only few of the several ones which benefit from autonomous and modern smart devices. Due to their tasks, almost all of these devices are battery powered, with some of them for which it is hard to preventively maintain it. Most of the works which tackles this problem rely on processes which could be unpractical in the real world due to complexity, time or cost constraints. In this paper we propose a novel methodology which leverages data obtained from normal charge and discharge cycles to diagnose the current battery for power fade faults and possibly perform maintenance before service interruption occurs. Tests performed on a real dataset demonstrate the feasibility of our approach.

2. Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenarios

   Federico Montori, and Luca Bedogni

   Pervasive Mob. Comput., 2023

   Abs DOI

   The recognition of the activity of texting while driving is an open problem in literature and it is crucial for the security within the scope of automotive. This can bring to life new insurance policies and increase the overall safety on the roads. Many works in literature leverage smartphone sensors for this purpose, however it is shown that these methods take a considerable amount of time to perform a recognition with sufficient confidence. In this paper we propose to leverage the smartphone front camera to perform an image classification and recognize whether the subject is seated in the driver position or in the passenger position. We first applied standalone Convolutional Neural Networks with poor results, then we focused on object detection-based algorithms to detect the presence and the position of discriminant objects (i.e. the security belts and the car win-dow). We then applied the model over short videos by classifying frame by frame until reaching a satisfactory confidence. Results show that we are able to reach around 90 % accuracy in only few seconds of the video, demonstrating the applicability of our method in the real world.

3. GreenCrowd: Toward a Holistic Algorithmic Crowd Charging Framework

   Theofanis P. Raptis, and Luca Bedogni

   IEEE Pervasive Comput., 2023

   Abs DOI

   Pervasive and ubiquitous applications provide novel and exciting services leveraging on a multitude of data obtained from people’s devices, adapting the computation to the context in which the user currently is. This improves the service quality of these applications, which can provide a more tailored configuration of the application itself depending on the user context and needs. In these scenarios privacy is of paramount importance, since users must be also be protected against the misuse of their personal data. Analyzing ubiquitous systems in terms of service quality and privacy issues is however a challenging task, due to the heterogeneity of the possible attacks, which makes it difficult to compare two applications. In this paper we propose a novel methodology to jointly evaluate the service quality and the privacy issues in ubiquitous applications in an extensible and comparable way, building on the data available in each part of the system to be analyzed, and defining service qualities and privacy issues so that they can be easily re-used in other analyses. Our evaluation on a candidate application highlights the benefits of our proposal, showing the dependency between privacy levels and service quality, and paving the way for a novel methodology for the definition of these scenarios.

4. Design and Development of a Mobile Dapp for Mobile Crowdsensing over EVM-enabled Blockchains

   Lorenzo Gigli, Federico Montori, Giacomo Galletti, and 2 more authors

   In Proceedings of the Fifth ACM International Workshop on Blockchain-enabled Networked Sensor Systems, BlockSys 2023, Istanbul, Turkiye, 12 November 2023, 2023

   Abs DOI

   Mobile crowdsensing has rapidly become an interesting and useful methodology to collect data in modern smart cities, thanks to the pervasiveness of users mobile devices. Although there are many different proposals, opportunistic and participatory mobile crowdsensing are the most popular ones. They share a common goal, but require a different effort from the user, which often results in increased costs for the service provider. In this work we forecast user participation in mobile crowdsensing by leveraging a large dataset obtained from a real world application, which is key to understand whether there are areas in a city which need additional data obtained through raised incentives for participants or by other means. We then build a custom regressor trained on the dataset we have, which spans across several years in different cities in Italy, to predict the amount of reports in a given area at a given time. This allows service providers to preventively issue participatory tasks for workers in areas which do not meet a minimum number of measurements. Our results indicate that our model is able to predict the number of reports in an area with an average mean error depending on the precision needed, in the order of 10% for areas with a low number of reports.

5. Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations

   Emanuele Zarfati, and Luca Bedogni

   In 20th IEEE Consumer Communications & Networking Conference, CCNC 2023, Las Vegas, NV, USA, January 8-11, 2023, 2023

   Abs DOI

   Wearable sensors and the plethora of Internet of Things devices are revolutionizing several aspects of everyday lives. In this domain, health monitoring applications are raising interest, thanks to their ability to track the vital parameters of the user wearing the device, and recognizing in advance potential issues health. Most of these solutions often require an internet connection to offload the data to an edge server, although this may not always be present, or use highly complex models which do not fit on constrained wearable devices. In this paper we propose a novel algorithm which tracks simple features in the ECG signal locally to the wearable device, with a lower memory footprint and computation resources needed compared to other proposal. Our extensive performance evaluation and comparison with the state of the art confirms the viability of our approach, as our proposal achieves more than 99% in accuracy on average.

6. Texting and Driving Recognition leveraging the Front Camera of Smartphones

   Federico Montori, Marco Spallone, and Luca Bedogni

   In 20th IEEE Consumer Communications & Networking Conference, CCNC 2023, Las Vegas, NV, USA, January 8-11, 2023, 2023

   Abs DOI

   The industry of the future, namely Industry 4.0, is built on top of novel and automated technologies, which optimize the production process by empowering industrial machines and process with novel and tailored software components. Artificial Intelligence algorithms enable to automate and improve traditional operations in industry, by learning from available data patterns and offering optimized operations. This scenario presents different research challenges: at first, there is the need for an architecture which can be customized depending on the specific task; at second, the knowledge of the system should grow together with the users, which need to instruct it according to their needs. Moreover, the real deployment of these system still faces challenges such as the latency and accuracy. We center our analysis on the Automated Optical Inspection task, and we present a novel architecture which faces such challenges. Our results show how these systems can be deployed in real scenarios and under which constraints they need to operate.

7. A Joint Evaluation Methodology for Service Quality and User Privacy in Location Based Systems

   Luca Bedogni, Chiara Franceschini, and Federico Montori

   In Proceedings of the 2023 ACM Conference on Information Technology for Social Good, GoodIT 2023, Lisbon, Portugal, September 6-8, 2023, 2023

   Abs DOI

   —Internet of Things (IoT) devices are available in a multitude of scenarios, and provide constant, contextual data which can be leveraged to automatically reconfigure and optimize smart environments. To realize this vision, Artificial Intelligence (AI) and deep learning techniques are usually employed, however they need large quantity of data which is often not feasible in IoT scenarios. Digital Twins (DTs) have recently emerged as an effective way to replicate physical entities in the digital domain, to allow for simulation and testing of models and services. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies Deep Reinforcement Learning (DRL) techniques on real time data. We implement our system in a real deployment, and test it along with a legacy system. Our findings show that the benefits of having a digital twin, specifically for DRL models, allow for faster convergence and finer tuning.

8. Towards User Behavior Forecasting in Mobile Crowdsensing Applications

   Luca Bedogni, Matteo Buferli, and Davide Marchi

   In Proceedings of the 2023 ACM Conference on Information Technology for Social Good, GoodIT 2023, Lisbon, Portugal, September 6-8, 2023, 2023

   Abs DOI

   The Internet of Things and more recently the Web of Things are changing how we interact with devices. The possibilities and novel services they provide enables the users to perform automatic operations and to monitor data of interest. Although many operations are performed autonomously by devices, there is still the need for the user to understand the data provided, and to configure their own services according to it. In this work we explore the possibility for devices to autonomously organize and understand the effects of the actions on the scenario, and provide a better status of the system. We do so by presenting a novel architecture, and developing a Q-learning algorithm which learns from the different statuses in which the system is. Our results indicate that devices with no prior knowledge of each other may eventually collaborate to provide a novel service to the end user, without any human intervention, and eventually achieve a better system status.

9. Computation Efficient ECG Classification on Resource Constrained Devices

   Andrea Arigliano, Andrea Malagoli, and Luca Bedogni

   In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023, Atlanta, GA, USA, March 13-17, 2023, 2023

   Abs DOI

   Wearable sensors and the plethora of Internet of Things devices are revolutionizing several aspects of everyday lives. In this domain, health monitoring applications are raising interest, thanks to their ability to track the vital parameters of the user wearing the device, and recognizing in advance potential issues health. Most of these solutions often require an internet connection to offload the data to an edge server, although this may not always be present, or use highly complex models which do not fit on constrained wearable devices. In this paper we propose a novel algorithm which tracks simple features in the ECG signal locally to the wearable device, with a lower memory footprint and computation resources needed compared to other proposal. Our extensive performance evaluation and comparison with the state of the art confirms the viability of our approach, as our proposal achieves more than 99% in accuracy on average.

10. Flexible Automated Optical Inspection Architecture for Industry 4.0

    Filippo Morselli, Luca Bedogni, Michele Fantoni, and 1 more author

    In 9th IEEE World Forum on Internet of Things, WF-IoT 2023, Aveiro, Portugal, October 12-27, 2023, 2023

    Abs DOI

    The industry of the future, namely Industry 4.0, is built on top of novel and automated technologies, which optimize the production process by empowering industrial machines and process with novel and tailored software components. Artificial Intelligence algorithms enable to automate and improve traditional operations in industry, by learning from available data patterns and offering optimized operations. This scenario presents different research challenges: at first, there is the need for an architecture which can be customized depending on the specific task; at second, the knowledge of the system should grow together with the users, which need to instruct it according to their needs. Moreover, the real deployment of these system still faces challenges such as the latency and accuracy. We center our analysis on the Automated Optical Inspection task, and we present a novel architecture which faces such challenges. Our results show how these systems can be deployed in real scenarios and under which constraints they need to operate.

11. A Web of Things Architecture for Digital Twin Creation and Model-Based Reinforcement Control

    Luca Bedogni, and Federico Chiariotti

    CoRR, 2023

    Abs DOI

    —Internet of Things (IoT) devices are available in a multitude of scenarios, and provide constant, contextual data which can be leveraged to automatically reconfigure and optimize smart environments. To realize this vision, Artificial Intelligence (AI) and deep learning techniques are usually employed, however they need large quantity of data which is often not feasible in IoT scenarios. Digital Twins (DTs) have recently emerged as an effective way to replicate physical entities in the digital domain, to allow for simulation and testing of models and services. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies Deep Reinforcement Learning (DRL) techniques on real time data. We implement our system in a real deployment, and test it along with a legacy system. Our findings show that the benefits of having a digital twin, specifically for DRL models, allow for faster convergence and finer tuning.

12. Does the venue of scientific conferences leverage their impact? A large scale study on Computer Science conferences.

    Luca Bedogni, Giacomo Cabri, Riccardo Martoglia, and 1 more author

    2023

    Abs DOI

    This paper proposes a groundbreaking architecture that reimagines Industry 5.0, emphasizing human-centric technological integration via the Web of Things (WoT) standard. Our approach innovatively digitizes human operators and machinery, creating a responsive industrial ecosystem attentive to real-time human conditions. Central to this is the Operator Thing (OT), a digital replica representing the human operator’s status and needs. This system not only recognizes operator stress and discomfort but intelligently adjusts, ensuring optimal human-machine synergy. Our methodology extends to redefining operational parameters and tasks in response to human states, balancing well-being with production efficiency. The ultimate goal is a transformative, adaptive, and empathetic Industry 5.0 environment, validated through rigorous interdisciplinary evaluation.

2022

1. Location Contact Tracing: Penetration, Privacy, Position, and Performance

   Luca Bedogni, Federico Montori, and Flora D. Salim

   Digit. Gov. Res. Pract., 2022

   Abs DOI

   The 2020 COVID-19 pandemic radically changed the world and how people interact, move, and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTAs) were proposed to digitally trace contacts between individuals while gradually releasing mobility constraints mandated to contain the spread of disease. General concern for privacy regarding the use of GPS data shifted the efforts toward distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are and to what extent the spread is progressing and at what pace. In addition to these issues, in this work we take a closer look at the major pillars of MCTA: Penetration, Privacy, Position, and Performance. We focus on (i) how the penetration rate affects the ability of a tracing application to work; (ii) the proposal of a novel method of tracing, which builds on the GPS technology; (iii) how the position of infections is beneficial to rapidly reduce the infection; and (iv) the discussion of the effects of such paradigms in different scenarios.

2. Enabling Green Crowdsourced Social Delivery Networks in Urban Communities

   Kevin Choi, Luca Bedogni, and Marco Levorato

   Sensors, 2022

   Abs DOI

   With the ever-increasing popularity of wearable devices, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. These data are currently used primarily for route discovery and mobile context awareness, as it provides precise and updated information about urban dynamics. We leverage these data to build ad hoc transportation flows, and we present a novel model that creates delivery networks from these zero-emission transportation flows. We evaluate the model using data from two popular datasets, and our results indicate that such networks are indeed possible, and can help reduce traffic, emissions, and delivery times. Moreover, we demonstrate how our results can be consistently reproduced in different cities with different subsets of carriers. We then extend our work into predicting routes of vehicles, hence possible delivery flows, based on the traces history. We conclude this paper by laying the groundwork for a future real-world study.

3. A Web Of Things Context-Aware IoT System leveraging Q-learning

   Luca Bedogni, and Francesco Poggi

   In 19th IEEE Annual Consumer Communications & Networking Conference, CCNC 2022, Las Vegas, NV, USA, January 8-11, 2022, 2022

   Abs DOI

   The Internet of Things and more recently the Web of Things are changing how we interact with devices. The possibilities and novel services they provide enables the users to perform automatic operations and to monitor data of interest. Although many operations are performed autonomously by devices, there is still the need for the user to understand the data provided, and to configure their own services according to it. In this work we explore the possibility for devices to autonomously organize and understand the effects of the actions on the scenario, and provide a better status of the system. We do so by presenting a novel architecture, and developing a Q-learning algorithm which learns from the different statuses in which the system is. Our results indicate that devices with no prior knowledge of each other may eventually collaborate to provide a novel service to the end user, without any human intervention, and eventually achieve a better system status.

4. A Hierarchical Architectural Model for IoT End-User Service Composition

   Federico Montori, Vincenzo Armandi, and Luca Bedogni

   In 19th IEEE Annual Consumer Communications & Networking Conference, CCNC 2022, Las Vegas, NV, USA, January 8-11, 2022, 2022

   Abs DOI

   The Internet of Things is permeating our everyday life and the number of sensors and actuators around us is increasing at an exponential pace. Data generated by such heterogeneous devices is hard to organize, therefore, in pervasive scenarios like Smart Cities, there is an increasing need for service infrastructures that play the role of intermediary between citizen and things. Often, end users call for customized services that are tailored to their specific need rather than general-purpose ones. For this reason, in this paper we propose a service architecture based on End-User Service Composition (EUSC), through which individuals can aggregate primary sources of data to compose services. Furthermore, we investigate the requirements for service reusability and inherently leverage a hierarchical paradigm by introducing a specific class of composition languages. Finally, we show our Proof-of-Concept (PoC) middleware implementation, namely SenSquare, to show how this is achievable in a real deployment through visual programming, specifically illustrating how hierarchization is achieved.

5. Re-identification Attack based on Few-Hints Dataset Enrichment for Ubiquitous Applications

   Andrea Artioli, Luca Bedogni, and Mauro Leoncini

   In 8th IEEE World Forum on Internet of Things, WF-IoT 2022, Yokohama, Japan, October 26 - Nov. 11, 2022, 2022

   Abs DOI

   Ubiquitous and pervasive applications record a large amount of data about users, to provide context-aware and tailored services. Although this enables more personalized applications, it also poses several questions concerning the possible misuse of such data by a malicious entity, which may discover private and sensitive information about the users themselves. In this paper we propose an attack on ubiquitous applications pseudo-anonymized datasets which can be leaked or accessed by the attacker. We enrich the data with true information which the attacker can obtain from a multitude of sources, which will eventually spark a chain reaction on the records of the dataset, possibly re-identifying users. Our results indicate that through this attack, and with few hints added to the dataset, the possibility of re-identification are considerable, achieving more than 70% re-identified users on a public available dataset. We compare our proposal with the state of the art, showing the improved performance figures obtained thanks to the graph-modeling of the dataset records and the novel hint structure.

6. WISE: A Semantic and Interoperable Web of Things Architecture for Smart Environments

   Luca Bedogni, Sebastiano Manfredini, Francesco Poggi, and 1 more author

   In 8th IEEE World Forum on Internet of Things, WF-IoT 2022, Yokohama, Japan, October 26 - Nov. 11, 2022, 2022

   Abs DOI

   The rapid proliferation of Internet of Things devices has led to a number of different standards and technologies which offer novel and exciting services. One of the key aspect of the Internet of Things is its ubiquitness, as devices may spontaneously form networks and leave them possibly in short time frames. This is the case of Smart Environments such as Smart Homes, in which users carry a set of devices like wearables and mobile applications to monitor their behavior and provide contextual services. However, the interoperability and seamless interaction of different devices is yet to be fully realized. In this paper we propose WISE, a framework that leverages the Web of Thing architecture and Semantic technologies to overcome technical and conceptual interoperability difficulties and enables the creation of cooperative Smart Environments that self-adapt on the basis of users’ preferences. The use of Semantic technologies enables to understand which devices can provide the needed affordances to meet the user preferences, while the WoT architecture is leveraged to access devices in a standardized manner. We also propose a reference implementation based on off-the-shelf devices which demonstrate the feasibility of WISE.

7. Pedometers for Smartphones: Analysis and Comparison of Real-Time Algorithms

   Giacomo Neri, Federico Montori, Lorenzo Gigli, and 3 more authors

   In 8th IEEE World Forum on Internet of Things, WF-IoT 2022, Yokohama, Japan, October 26 - Nov. 11, 2022, 2022

   Abs DOI

   The recent years have witnessed the rise of an enormous number of software algorithms that implement pe-dometers (or step counters), which led to the development of several context-aware IoT-based smartphone apps for sports and healthcare, among others. While the number of scientific works in this context is high, there is no comparison study that analyzes the different proposal at implementation level. In this paper we first perform a literature review of software implementations of pedometers for smartphones and then classify them into a taxonomy. With this, we highlight the similarities of their scheme, which is based on a number of defined steps to be applied in a pipeline. We then develop a smartphone application that implements all the configurations of these steps found in literature and evaluates them in various scenarios. Finally, we present comparative results obtained by running extensive and real tests that show the importance of a carefully designed filtering step.

8. SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems

   Delaram Amiri, Janne Takalo-Mattila, Luca Bedogni, and 2 more authors

   In 23rd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022, Belfast, United Kingdom, June 14-17, 2022, 2022

   Abs DOI

   Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a "semantic compression" framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive "semantic" compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage.

2021

1. Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

   Luca Bedogni, Shakila Khan Rumi, and Flora D. Salim

   Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2021

   Abs DOI

   In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack.

2. IoT End-User Service Composition via a Visual Programming Interface

   Federico Montori, Vincenzo Armandi, and Luca Bedogni

   In IEEE International Conference on Smart Computing, SMARTCOMP 2021, Irvine, CA, USA, August 23-27, 2021, 2021

   Abs DOI

   Sensory data generated around us in the context of IoT is huge and heterogeneous. To fully unleash the potential of IoT Open Data there is a need for service infrastructures that facilitate the interaction of users with such data, especially when they are able to customize such services to fit their needs. In this paper we propose to use our tool SenSquare for IoT End-User Service Composition, by presenting its main features and its recent advances towards providing a data historian and importing other services, as well as evaluating the performance of its implementation in parallel.

3. Dataset for the Article "Does the Venue of Scientific Conferences Leverage their Impact? A Large Scale study on Computer Science Conferences" (Version 1)

   Luca Bedogni, Giacomo Cabri, Riccardo Martoglia, and 1 more author

   May 2021

   Accessed on YYYY-MM-DD.

   Abs DOI

   Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Depending on the degree of involvement of users, MCS systems can be participatory, opportunistic or hybrid, which combines strengths of above approaches. Typically, a large number of participants is required to make a sensing campaign successful which makes impractical to build and deploy large testbeds to assess the performance of MCS phases like data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we focus on hybrid MCS and extend CrowdSenSim 2.0 in order to support such systems. Specifically, we propose an algorithm for efficient re-route users that would offer opportunistic contribution towards the location of sensitive MCS tasks that require participatory-type of sensing contribution. We implement such design in CrowdSenSim 2.0, which by itself extends the original CrowdSenSim by featuring a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms.

4. Dataset for the Article "Does the Venue of Scientific Conferences Leverage their Impact? A Large Scale study on Computer Science Conferences" (Version 2)

   Luca Bedogni, Giacomo Cabri, Riccardo Martoglia, and 1 more author

   May 2021

   Accessed on YYYY-MM-DD.

   Abs DOI

   With the ever-increasing popularity of fitness trackers, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. This data is currently used primarily for route discovery and personal fitness tracking, but it may also be leveraged to build ad-hoc transportation flows. We present a novel model that creates delivery networks from these zero-emission transportation flows, and we evaluate the model using data from two popular datasets. Our results indicate that such networks are indeed possible, and can help reduce traffic, emissions, and delivery times. Moreover, we demonstrate how our results can be consistently reproduced in different cities with different subsets of carriers.

5. Does the Venue of Scientific Conferences Leverage their Impact? A

   Luca Bedogni, Giacomo Cabri, Riccardo Martoglia, and 1 more author

   CoRR, May 2021

   Abs DOI

   PurposeConferences bring scientists together and provide one of the most timely means for disseminating new ideas and cutting-edge works. The importance of conferences in many scientific areas is testified by quantitative indexes. The main goal of this paper is to investigate a novel research question: is there any correlation between the impact of scientific conferences and the venue where they took place?Design/methodology/approachTo measure the impact of conferences, the authors conducted a large scale analysis on the bibliographic data extracted from 3,838 Computer Science conference series and over 2.5 million papers spanning more than 30 years of research. To quantify the “touristicity” of a venue, the authors exploited indexes about the attractiveness of a venue from reports of the World Economic Forum, and have extracted four country-wide and two city-wide touristic indexes, which measure the attractiveness and the touristicity of any country or city.FindingsThe authors found out that the two aspects are related, and the correlation with conference impact is stronger when considering country-wide touristic indexes, achieving a correlation value of more than 0.5 when considering the average citations, and more than 0.8 when considering the total citations. Moreover the almost linear correlation with the Tourist Service Infrastructure index attests the specific importance of tourist/accommodation facilities in a given country.Research limitations/implicationsThere are two main limitations of this work: (1) the use of citations to evaluate the attractiveness of the conferences and (2) the difficulty to formally define the touristic attractiveness of a venue.Practical implicationsStarting from the results concerning the correlation between different touristicity indicators and the outcome of a conference in terms of citations, it would be possible to support conference organizers in their decisions. For instance, they could plan in advance conference venues considering the same touristicity indicators, comparing different options and selecting cities which have high scores. This will allow for rapid planning of a conference venue, encompassing the easiness of travel and the attractivity of a venue, hence increasing the potential outcomes of the conference.Social implicationsRegarding the social implications, this study will enable the possibility for municipalities and conference organizers to understand what it can be improved in a specific venue to make it more attractive. This may include better transport connections or selecting cities which show a high potential regarding the touristicity index. Regarding the willingness of a researcher to submit a paper to a specific conference, it would be unaltered, meaning that what the results show is that there is already a mental process, before submitting a paper to a conference, which considers these indicators.Originality/valueThis is the first attempt to focus on the relationship of venue characteristics to conference papers. The results open up new possibilities, such as supporting conference organizers in their organization efforts.

6. Dataset for the Article "Does the Venue of Scientific Conferences Leverage their Impact? A Large Scale study on Computer Science Conferences".

   Luca Bedogni, Giacomo Cabri, Riccardo Martoglia, and 1 more author

   May 2021

   Abs DOI

   Recent studies explore the possibility of detecting events in a room via Wi-Fi Sensing. This practice exploits the interaction between waves carrying Wi-Fi signals and the elements present in an environment. These interactions are called Channel State Information (CSI) and can be analyzed and exploited to infer information about the environment, such as “device-free” Human Activity Recognition, Human Identification, and more. Considering identification, we recently saw an increasing trend in the usage of low-end devices such as ESP32. Being small and low-power, they are cheap and versatile, however, the quality of the collected data is inferior. In this work, we use state-of-the-art tools to perform Human Identification using the ESP32. Software is created to act as an interface between the collected data and the algorithms suitable for Wi-Fi Sensing. To evaluate the final design, we performed a data collection in a controlled environment. The experiments show an accuracy of 95% in distinguishing two users while 74% in distinzuishing three.

2020

1. Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator

   Federico Montori, Luca Bedogni, Claudio Fiandrino, and 2 more authors

   Comput. Commun., May 2020

   Abs DOI

   Crowdsensing is rapidly becoming an interesting approach for scenarios in which a significant amount of data is needed and a static infrastructure is not a viable option due to cost or other challenges. Although users collect data without any direct cost, it is common to reward them depending on the amount and quality of the data they provide. However, as this data also carries sensitive geolocation information, it also exposes the users to privacy concerns, if such data is accessed by a malicious entity. Geolocation information can disclose information about the habit of the user and his or her places of interest, however, in many cases, such information is crucial for the purpose of the application and cannot be omitted nor distorted. In this work, we present a novel framework for opportunistic MCS scenarios focused on maintaining the privacy of the users while rewarding them for their collected and geolocated data. We evaluate our proposal on real datasets, quantifying its benefits over other methodologies.

2. Special issue on "Crowd-sensed Big Data for Internet of Things Services"

   Luca Bedogni, Salil S. Kanhere, Hongyi Wu, and 1 more author

   Pervasive Mob. Comput., May 2020

   Abs DOI

   IoT is spreading heavily in many use cases that surround our everyday life; however, existing IoT ecosystems are still behaving as close islands with little interoperability with each other. Recent research efforts tend to propose new architectures and standards to which private customers and companies producing data are supposed to adhere in order to make them consistent. However, such entities have their own vested interests that hinder data integration. We instead leverage information acquisition through Collective Awareness Paradigms (CAPs) such as Open Data and Mobile Crowdsensing (MCS) in order to use what is already available. As a proof of concept, we developed SenSquare, a prototype IoT architecture and platform for Smart Cities and environmental monitoring that gathers raw data through CAPs, adapts it to a common semantic and composes customizable flexible services. Inexperienced users can generate their own services using an easy visual programming plugin designed around a customized language. Furthermore, we test the platform on a real world use case.

3. Towards Green Crowdsourced Social Delivery Networks: A Feasibility Study

   Kevin Choi, Luca Bedogni, and Marco Levorato

   In IEEE Global Communications Conference, GLOBECOM 2020, Virtual Event, Taiwan, December 7-11, 2020, May 2020

   Abs DOI

   With the ever-increasing popularity of fitness trackers, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. This data is currently used primarily for route discovery and personal fitness tracking, but it may also be leveraged to build ad-hoc transportation flows. We present a novel model that creates delivery networks from these zero-emission transportation flows, and we evaluate the model using data from two popular datasets. Our results indicate that such networks are indeed possible, and can help reduce traffic, emissions, and delivery times. Moreover, we demonstrate how our results can be consistently reproduced in different cities with different subsets of carriers.

4. A Privacy Preserving Framework for Rewarding Users in Opportunistic Mobile Crowdsensing

   Federico Montori, and Luca Bedogni

   In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA, March 23-27, 2020, May 2020

   Abs DOI

   Crowdsensing is rapidly becoming an interesting approach for scenarios in which a significant amount of data is needed and a static infrastructure is not a viable option due to cost or other challenges. Although users collect data without any direct cost, it is common to reward them depending on the amount and quality of the data they provide. However, as this data also carries sensitive geolocation information, it also exposes the users to privacy concerns, if such data is accessed by a malicious entity. Geolocation information can disclose information about the habit of the user and his or her places of interest, however, in many cases, such information is crucial for the purpose of the application and cannot be omitted nor distorted. In this work, we present a novel framework for opportunistic MCS scenarios focused on maintaining the privacy of the users while rewarding them for their collected and geolocated data. We evaluate our proposal on real datasets, quantifying its benefits over other methodologies.

5. Delivering IoT Smart Services through Collective Awareness, Mobile Crowdsensing and Open Data

   Federico Montori, Luca Bedogni, Gianluca Iselli, and 1 more author

   In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA, March 23-27, 2020, May 2020

   Abs DOI

   IoT is spreading heavily in many use cases that surround our everyday life; however, existing IoT ecosystems are still behaving as close islands with little interoperability with each other. Recent research efforts tend to propose new architectures and standards to which private customers and companies producing data are supposed to adhere in order to make them consistent. However, such entities have their own vested interests that hinder data integration. We instead leverage information acquisition through Collective Awareness Paradigms (CAPs) such as Open Data and Mobile Crowdsensing (MCS) in order to use what is already available. As a proof of concept, we developed SenSquare, a prototype IoT architecture and platform for Smart Cities and environmental monitoring that gathers raw data through CAPs, adapts it to a common semantic and composes customizable flexible services. Inexperienced users can generate their own services using an easy visual programming plugin designed around a customized language. Furthermore, we test the platform on a real world use case.

6. Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

   Luca Bedogni, Shakila Khan Rumi, and Flora D. Salim

   CoRR, May 2020

   Abs

   Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Typically, a large number of participants is required to make a sensing campaign successful. For such a reason, it is often not practical for researchers to build and deploy large testbeds to assess the performance of frameworks and algorithms for data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we present CrowdSenSim 2.0, a significant extension of the popular CrowdSenSim simulation platform. CrowdSenSim 2.0 features a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms. All these improvements boost the performances of the simulator and make the runtime execution and memory utilization significantly lower, also enabling the support for larger simulation scenarios. We demonstrate retro-compatibility with the older platform and evaluate as a case study a stateful data collection algorithm.

7. Identification of Social Aspects by Means of Inertial Sensor Data

   Luca Bedogni, and Giacomo Cabri

   Inf., May 2020

   Abs DOI

   Today’s applications and providers are very interested in knowing the social aspects of users in order to customize the services they provide and to be more effective. Among the others, the most frequented places and the paths to reach them are information that turns out to be very useful to define users’ habits. The most exploited means to acquire positions and paths is the GPS sensor, however it has been shown how leveraging inertial data from installed sensors can lead to path identification. In this work, we present a Computationally Efficient algorithm to Reconstruct Vehicular Traces (CERT), a novel algorithm which computes the path traveled by a vehicle using accelerometer and magnetometer data. We show that by analyzing data obtained through the accelerometer and the magnetometer in vehicular scenarios, CERT achieves almost perfect identification for medium and small sized cities. Moreover, we show that the longer the path, the easier it is to recognize it. We also present results characterizing the privacy risks depending on the area of the world, since, as we show, urban dynamics play a key role in the path detection.

2019

1. CrowdSenSim 2.0: a Stateful Simulation Platform for Mobile Crowdsensing in Smart Cities

   Federico Montori, Emanuele Cortesi, Luca Bedogni, and 3 more authors

   In Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2019, Miami Beach, FL, USA, November 25-29, 2019, May 2019

   Abs DOI

   Mobile devices are carried by many individuals in the world, which use them to communicate with friends, browse the web, and use different applications depending on their objectives. Normally the devices are equipped with integrated sensors such as accelerometers and magnetometers, through which application developers can obtain the inertial values of the dynamics of the device, and infer different behaviors about what the user is performing. As users type on the touch keyboard with one hand, they also tilt the smartphone to reach the area to be pressed. In this paper, we show that using these zero-permissions sensors it is possible to obtain the area pressed by the user with more than 80% of accuracy in some scenarios. Moreover, correlating subsequent areas related to keyboard keys together, it is also possible to determine the words typed by the user, even for long words. This would help understanding what user are doing, though raising privacy concerns.

2. Permission-free Keylogging through Touch Events Eavesdropping on Mobile Devices

   Luca Bedogni, Andrea Alcaras, and Luciano Bononi

   In IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, May 2019

   Abs DOI

   Location based services are commonly used by several mobile applications and services, to provide content related to the area in which the user is located. This enables services such as navigation, particularly useful for vehicular applications, though possibly exposing private information about the user, which has to explicitly grant the location permission. However, smartphone have also many other sensors off the shelf, which currently do not require any permission to be used, and may be leveraged to track the users movements, hence the location, thus raising potentially serious privacy issues. In this paper we present a study which shows that by analyzing data obtained through the accelerometer and the magnetometer, it is possible to achieve less than 50 meters of localization accuracy even for long journeys, and 95% of accuracy on the road identification.

3. Vehicular Route Identification Using Mobile Devices Integrated Sensors

   Luca Bedogni, and Luciano Bononi

   In IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, May 2019

   Abs DOI

   Texting while Driving has been reported as one of the major sources of inattention by car drivers, leading to an increased probability of severe road accidents. In fact, notifications, messages and other interactions with mobile devices may make the driver unaware of road and traffic events. To prevent or mitigate this issue, solutions have been proposed that either block the smartphone when inside the vehicle or recognize the activity to issue monetary fines at a later time. This paper proposes a classification framework capable to identify the location of a device within the vehicle using data from integrated sensors. This allow more selective countermeasures targeted specifically to mobile devices used by the driver, rather than by any person inside the vehicle. The framework extracts sensor data from the smartphone, computes ad-hoc features and feeds them to a neural network. Different from prior work, we demonstrate that accurate detection can be achieved even using only one device by combining subsequent turns of the vehicle.

4. Texting and Driving Recognition Exploiting Subsequent Turns Leveraging Smartphone Sensors

   Luca Bedogni, Octavian Bujor, and Marco Levorato

   In 20th IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks", WoWMoM 2019, Washington, DC, USA, June 10-12, 2019, May 2019

   Abs DOI

   Texting while Driving has been reported as one of the major sources of inattention by car drivers, leading to an increased probability of severe road accidents. In fact, notifications, messages and other interactions with mobile devices may make the driver unaware of road and traffic events. To prevent or mitigate this issue, solutions have been proposed that either block the smartphone when inside the vehicle or recognize the activity to issue monetary fines at a later time. This paper proposes a classification framework capable to identify the location of a device within the vehicle using data from integrated sensors. This allow more selective countermeasures targeted specifically to mobile devices used by the driver, rather than by any person inside the vehicle. The framework extracts s

2018

1. A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring

   Federico Montori, Luca Bedogni, and Luciano Bononi

   IEEE Internet Things J., May 2018

   Abs DOI

   Nowadays, Location Based Services (LBS) are fore- seen as a fundamental building block of modern mobile applications and services. Important examples of LBS concerns indoor environments in which GPS technology cannot be used. On the other hand, the great diffusion of pervasive Mobile Devices (MDs) as smartphones and tablets has enabled many positioning techniques, such as WiFi FingerPrinting (FP), that exploits all the MD’s embedded sensors. This paper proposes and investigates the performance of a method exploiting a WiFi FP algorithm for indoor localization fed with information from the barometer to estimate the floor in which the MD is located. Our results, carried out in indoor areas at the University of Genoa (UniGE) and at the University of Bologna (UniBO), show that when more than 5 Access Points (APs) are used the proposed 3D positioning system is able to accurately localize the user with an error below 2 and 1.2 and meters for the UniBO and UniGE case, respectively.

2. Machine-to-machine wireless communication technologies for the Internet of Things: Taxonomy, comparison and open issues

   Federico Montori, Luca Bedogni, Marco Di Felice, and 1 more author

   Pervasive Mob. Comput., May 2018

   Abs DOI

   Upcoming mobile network technologies developed in the context of 5G and DSRC are expected to finally legitimize direct data transfers among vehicles as a standard communication paradigm. We investigate fundamental properties of the topology of vehicular networks built on top of these emerging vehicle-to-vehicle communication technologies. Our study yields multiple elements of originality: (i) it addresses temporal connectivity, which has been poorly investigated despite a high relevance for vehicular network operations; (ii) it introduces exact but computationally efficient models of the temporal connectivity of vehicular networks; (iii) it evaluates the proposed models in urban settings that exhibit an unprecedented combination of dependability, scale and generality of vehicular mobility. This approach lets us unveil an apparent scale-and city-invariant law of temporal reachability in vehicular networks. Finally, we open our original scenarios to the research community, so as to ensure reproducibility of our results and foster further investigations of vehicular network performance.

3. Rising User Privacy Against Predictive Context Awareness Through Adversarial Information Injection

   Luca Bedogni, and Marco Levorato

   In IEEE Global Communications Conference, GLOBECOM 2018, Abu Dhabi, United Arab Emirates, December 9-13, 2018, May 2018

   Abs DOI

   Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smartphones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.

4. Dual-Mode Wake-Up Nodes for IoT Monitoring Applications: Measurements and Algorithms

   Luca Bedogni, Luciano Bononi, Roberto Canegallo, and 7 more authors

   In 2018 IEEE International Conference on Communications, ICC 2018, Kansas City, MO, USA, May 20-24, 2018, May 2018

   Abs DOI

   Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion’s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

5. WiFi Meets Barometer: Smartphone-Based 3D Indoor Positioning Method

   Igor Bisio, Andrea Sciarrone, Luca Bedogni, and 1 more author

   In 2018 IEEE International Conference on Communications, ICC 2018, Kansas City, MO, USA, May 20-24, 2018, May 2018

   Abs DOI

   Nowadays, Location Based Services (LBS) are fore- seen as a fundamental building block of modern mobile applications and services. Important examples of LBS concerns indoor environments in which GPS technology cannot be used. On the other hand, the great diffusion of pervasive Mobile Devices (MDs) as smartphones and tablets has enabled many positioning techniques, such as WiFi FingerPrinting (FP), that exploits all the MD’s embedded sensors. This paper proposes and investigates the performance of a method exploiting a WiFi FP algorithm for indoor localization fed with information from the barometer to estimate the floor in which the MD is located. Our results, carried out in indoor areas at the University of Genoa (UniGE) and at the University of Bologna (UniBO), show that when more than 5 Access Points (APs) are used the proposed 3D positioning system is able to accurately localize the user with an error below 2 and 1.2 and meters for the UniBO and UniGE case, respectively.

6. Temporal Reachability in Vehicular Networks

   Luca Bedogni, Marco Fiore, and Christian Glacet

   In 2018 IEEE Conference on Computer Communications, INFOCOM 2018, Honolulu, HI, USA, April 16-19, 2018, May 2018

   Abs DOI

   Upcoming mobile network technologies developed in the context of 5G and DSRC are expected to finally legitimize direct data transfers among vehicles as a standard communication paradigm. We investigate fundamental properties of the topology of vehicular networks built on top of these emerging vehicle-to-vehicle communication technologies. Our study yields multiple elements of originality: (i) it addresses temporal connectivity, which has been poorly investigated despite a high relevance for vehicular network operations; (ii) it introduces exact but computationally efficient models of the temporal connectivity of vehicular networks; (iii) it evaluates the proposed models in urban settings that exhibit an unprecedented combination of dependability, scale and generality of vehicular mobility. This approach lets us unveil an apparent scale-and city-invariant law of temporal reachability in vehicular networks. Finally, we open our original scenarios to the research community, so as to ensure reproducibility of our results and foster further investigations of vehicular network performance.

7. Custom Dual Transportation Mode Detection By Smartphone Devices Exploiting Sensor Diversity

   Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, and 2 more authors

   In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, Athens, Greece, March 19-23, 2018, May 2018

   Abs DOI

   Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smartphones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.

8. Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

   Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, and 2 more authors

   CoRR, May 2018

   Abs

   Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion’s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

2017

1. Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks

   Luca Bedogni, Angelo Trotta, Marco Di Felice, and 5 more authors

   IEEE/ACM Trans. Netw., May 2017

   Abs DOI

   Television (TV) white space (WS) constitutes a key technology to support the increasing worldwide growth of spectrum demand with several regulation standards that are already available for long- and medium-range communications. At the same time, estimations based on WS spectrum databases (SDBs) indicate that the availability of TVWS is often very limited in dense urban areas where spectrum resources are more needed. Therefore, the benefits provided by the utilization of TVWS have yet to be fully assessed. In this article, we rethink the utilization of TVWS in indoor communication environments through novel three-dimensional (3-D) spectrum-sharing mechanisms. Based on measurements that demonstrate the differences in terms of spectrum opportunities at different floors of the same building, we propose an underlay spectrum-sharing architecture to enable a per-building finegrained reuse of TV frequencies while protecting the operations of TV receivers in a neighborhood. We evaluate the effectiveness of the proposed spectrum-sharing architecture over several urban environments in Italy by taking into account many real characteristics of the scenarios. Our results demonstrate that through our architecture, more spectrum resources than what are reported in the SDB can be available for indoor scenarios, even in highly congested urban areas, paving the way to novel TVWS applications.

2. Indoor Use of Gray and White Spaces: Another Look at Wireless Indoor Communication

   Luca Bedogni, Fabio Malabocchia, Marco Di Felice, and 1 more author

   IEEE Veh. Technol. Mag., May 2017

   Abs DOI

   The Internet of Things (IoT) environments are no more a vision as they are already surrounding the everyday life of citizens. Its pervasive nature brings IoT ecosystems closer and closer to the end users, facilitating their domestic lives through home automation appliances and platforms. Several M2M communication technologies and data representation techniques have been standardized and often established by the manufacturers. For such reasons, many Home Automation Systems (HAS) are irreconcilable due to the incompatibility of their communication technologies. In order to take a step towards HAS interoperability, in this paper we propose RouteX, an experimental cross-technology platform able to manage home devices belonging to different networks and using different technologies. It leverages the potential of Service Oriented Architectures (SOA), providing the user with an abstraction layer over a multitude of home sensors and actuators. We also present a practical user interface, through which the user is able to administrate efficiently all his or her home appliances no matter which technology they use.

3. Achieving IoT Interoperability through a Service Oriented In-Home Appliance

   Federico Montori, Luca Bedogni, Filippo Morselli, and 1 more author

   In 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, December 4-8, 2017, May 2017

   Abs DOI

   Video streaming takes the lion’s share of network bandwidth, with a trend that will likely increase in the future. In the recent years, dynamic adaptive streaming has been developed to offer a smooth video stream with variable quality, depending on the performance of the network connection. At present, several content providers like Netflix, YouTube and Hulu, to name a few, already offer videos that can be streamed with a dynamic video quality. In this work we study the tradeoff of the video length to be downloaded, called segment size, and show the differences in terms of playback quality and reduced buffer outages. We then propose an algorithm able to exploit the network conditions, and adaptively select the video segment size to be downloaded. We analyze the benefits of our proposal in terms of increased playback quality and reduced buffer outages against classic fixed segment size solutions. Our result show that dynamically adapting the segment size can reduce buffer outages, while also increasing the quality streamed.

4. Dynamic segment size selection in HTTP based adaptive video streaming

   Luca Bedogni, Marco Di Felice, and Luciano Bononi

   In 2017 IEEE Conference on Computer Communications Workshops, INFOCOM Workshops, Atlanta, GA, USA, May 1-4, 2017, May 2017

   Abs DOI

   Many researchers have nowadays shown a paramount interest in the rising field of Mobile Crowdsensing (MCS). Such paradigm is considered an easy and cost-effective choice for observing phenomena of common interest within the scope of Smart Cities and environmental monitoring. Nevertheless, it brings along many issues, such as fostering participation, reducing the power consumption of end devices and granting coverage. In this paper we focus on the problem of data collection control, which aims to avoid data redundancy and useless power consuming data transfers while assuring a sufficient number of observations for the purpose of coverage. In particular, we design a probabilistic distributed algorithm that aims to achieve a total per-zone number of observations close to a defined amount, while maximizing the fairness among users. We provide both the analytical definition of our algorithm and the performance evaluation through extensive simulations, establishing our algorithm as a good baseline for a poorly investigated problem.

5. Distributed Data Collection Control in Opportunistic Mobile Crowdsensing

   Federico Montori, Luca Bedogni, and Luciano Bononi

   In Proceedings of the 3rd Workshop on Experiences with the Design and Implementation of Smart Objects, SmartObjects@MobiCom 2017, Snowbird, UT, USA, October 16, 2017, May 2017

   Abs DOI

   Nowadays billions of connected objects are publishing sensed data everyday and this number is expected to grow exponentially. In many cases IoT objects are battery powered and need to be energy efficient as the most important requirement in order to reduce battery replacement costs. Conversely, WiFi is still the predominant wireless technology, deployed in almost all everyday life environments and thus the easiest network type on top of which build an IoT ecosystem. In this paper we analyze the energy efficiency of constrained devices using WiFi, which is certainly a widely accepted technology, although not specifically designed for constrained devices. To perform our test, we use the ESP-12 SoC, which gained interest recently due to its low cost and capabilities. We test its performance by studying the battery duration of such device under different connectivity conditions, varying its authentication policy, its battery type and its duty cycle. We report results from laboratory tests and show that such devices can be an efficient compromise for low-cost low-energy scenarios using WiFi.

6. Is WiFi suitable for energy efficient IoT deployments? A performance study

   Federico Montori, Riccardo Contigiani, and Luca Bedogni

   In 3rd IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2017, Modena, Italy, September 11-13, 2017, May 2017

   Abs DOI

   In most countries the share of circulating electric vehicles (EV) is less than 1%. Beside cost issues, the detrimental factors that most discourage people from purchasing EVs are their limited range and the rather poor coverage area of the recharging infrastructure. This clearly indicates the need for higher investments in vehicular technologies and infrastructures; however, software services and applications also play a major role in the transition to electric mobility, e.g. to mitigate EV driver anxiety. In this paper we illustrate the recipe proposed by the European Internet of Energy (IoE) project toward interoperable services for large-scale EV mobility scenarios: it consists of a Service-Oriented Architecture (SoA) to collect and integrate data coming from the heterogeneous actors of the EV scenario thanks to a semantic data tier. On top of it, we describe the implementation of an advanced Route Planning service to compute the optimal path towards a destination according to user-defined parameters and goals; differently from other approaches, our tool integrates reservation mechanisms of charging slots, so that a complete planning of the itinerary can be produced. Effectiveness of the route planning service is evaluated on large-scale EV scenarios (e.g. the Italian Emilia-Romagna region) thanks to an innovative emulation-simulation platform.

7. Automotive Communications in LTE: A Simulation-Based Performance Study

   Federico Montori, Marco Gramaglia, Luca Bedogni, and 4 more authors

   In 86th IEEE Vehicular Technology Conference, VTC Fall 2017, Toronto, ON, Canada, September 24-27, 2017, May 2017

   Abs DOI

   Modern smartphones are nowadays equipped with a multitude of sensors, which extend their capabilities paving the way for a multitude of services. Among these, the ability to locate the device is exploited by many. While outdoor the GPS provides good accuracy, indoor localization is challenging to be performed with it, as buildings shadow the satellite signal. In particular, the barometric pressure sensor is often used to determine the altitude of the device from the ground floor, particularly for safety applications and indoor navigation. However, pressure changes during the day, and thus it is challenging to bind a static value to a specific altitude. In this work, we propose a self-adapting algorithm able to determine the height at which the device is in a building, by exploiting the barometric pressure. We implemented and tested our algorithm on an Android application, and we compared it against other techniques. We tested our proposal for three specific use-cases, and our results show the benefit of our proposal.

8. Performance Assessment and Feasibility Analysis of IEEE 802.15.4m Wireless Sensor Networks in TV Grayspaces

   Luca Bedogni, Andreas Achtzehn, Marina Petrova, and 2 more authors

   ACM Trans. Sens. Networks, May 2017

   Abs DOI

   In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility-aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data accelerometer/gyroscope with a novel meta-classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS-based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third-part mobile applications to access context-aware information in a transparent way. Copyright © 2016 John Wiley & Sons, Ltd.

2016

1. A Route Planner Service with Recharging Reservation: Electric Itinerary with a Click

   Luca Bedogni, Luciano Bononi, Marco Di Felice, and 2 more authors

   IEEE Intell. Transp. Syst. Mag., May 2016

   Abs DOI

   Real-world data are key to the implementation and validation of urban transport models, and their availability and accuracy can dramatically affect the reliability of the resulting estimates. This paper discusses the potential of open data as a mean to gain insights in urban mobility, so as to supplement traditional methodologies that are often complex and expensive. We propose a methodology-fully based on publicly accessible data-for the development of Origin-Destination Matrices (ODMs). The methodology uses as input (i) a baseline morning-peak-hour ODM and (ii) road traffic count data. We test our proposed approach in a real-world case study, i.e., the city of Bologna, Italy. We also employ open geospatial data, from socioeconomic sources, to validate the ODMs, and find that these reproduce the typical urban travel demand profile in the target city during a whole day. Our results demonstrate the suitability of the methodology for transport policy design and urban planning. We also discuss current difficulties of gathering open data and the lessons learned when attempting to leverage such data.

2. An Integrated Simulation Framework to Model Electric Vehicle Operations and Services

   Luca Bedogni, Luciano Bononi, Marco Di Felice, and 6 more authors

   IEEE Trans. Veh. Technol., May 2016

   Abs DOI

   After the digital TV switch-over, national spectrum regulators are considering opportunistic spectrum access techniques in the TV White Spaces (TVWS) frequency band. At present, the reference solution envisages the utilization of geolocation spectrum databases (GLDBs), in which spectrum availability is computed through complex propagation models. However, recent studies indicate that the used path loss model in GLDBs could be either inaccurate or too much conservative, possibly reducing the use of TVWS for opportunistic use by secondary networks. In this paper, we investigate the possibility to enhance the estimation accuracy of GLDBs with sensing reports produced by a swarm of Unmanned Aerial Scanning Vehicles (UASVs). These latter are able to explore the scenario in both space and frequencies, and to build a fine-grained shadowing map which can be used to tune the accuracy of propagation model used by GLDB. A novel distributed mobility algorithm is described for the sensing coverage of the scenario, and an aggregation mechanism for the map creation is illustrated. Simulation results confirm the effectiveness of our scheme in terms of TVWS detection accuracy and scenario coverage issues.

3. A Self-Adapting Algorithm Based on Atmospheric Pressure to Localize Indoor Devices

   Luca Bedogni, Fabio Franzoso, and Luciano Bononi

   In 2016 IEEE Global Communications Conference, GLOBECOM 2016, Washington, DC, USA, December 4-8, 2016, May 2016

   Abs DOI

   Modern smartphones are nowadays equipped with a multitude of sensors, which extend their capabilities paving the way for a multitude of services. Among these, the ability to locate the device is exploited by many. While outdoor the GPS provides good accuracy, indoor localization is challenging to be performed with it, as buildings shadow the satellite signal. In particular, the barometric pressure sensor is often used to determine the altitude of the device from the ground floor, particularly for safety applications and indoor navigation. However, pressure changes during the day, and thus it is challenging to bind a static value to a specific altitude. In this work, we propose a self-adapting algorithm able to determine the height at which the device is in a building, by exploiting the barometric pressure. We implemented and tested our algorithm on an Android application, and we compared it against other techniques. We tested our proposal for three specific use-cases, and our results show the benefit of our proposal.

4. Estimating urban mobility with open data: A case study in Bologna

   Valeria Caiati, Luca Bedogni, Luciano Bononi, and 3 more authors

   In IEEE International Smart Cities Conference, ISC2 2016, Trento, Italy, September 12-15, 2016, May 2016

   Abs DOI

   Real-world data are key to the implementation and validation of urban transport models, and their availability and accuracy can dramatically affect the reliability of the resulting estimates. This paper discusses the potential of open data as a mean to gain insights in urban mobility, so as to supplement traditional methodologies that are often complex and expensive. We propose a methodology-fully based on publicly accessible data-for the development of Origin-Destination Matrices (ODMs). The methodology uses as input (i) a baseline morning-peak-hour ODM and (ii) road traffic count data. We test our proposed approach in a real-world case study, i.e., the city of Bologna, Italy. We also employ open geospatial data, from socioeconomic sources, to validate the ODMs, and find that these reproduce the typical urban travel demand profile in the target city during a whole day. Our results demonstrate the suitability of the methodology for transport policy design and urban planning. We also discuss current difficulties of gathering open data and the lessons learned when attempting to leverage such data.

5. Enhancing TV White-Spaces Database with Unmanned Aerial Scanning Vehicles (UASVs)

   Angelo Trotta, Luca Bedogni, Marco Di Felice, and 2 more authors

   In Proceedings of the 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use, DroNet@MobiSys 2016, Singapore, June 26, 2016, May 2016

   Abs DOI

   After the digital TV switch-over, national spectrum regulators are considering opportunistic spectrum access techniques in the TV White Spaces (TVWS) frequency band. At present, the reference solution envisages the utilization of geolocation spectrum databases (GLDBs), in which spectrum availability is computed through complex propagation models. However, recent studies indicate that the used path loss model in GLDBs could be either inaccurate or too much conservative, possibly reducing the use of TVWS for opportunistic use by secondary networks. In this paper, we investigate the possibility to enhance the estimation accuracy of GLDBs with sensing reports produced by a swarm of Unmanned Aerial Scanning Vehicles (UASVs). These latter are able to explore the scenario in both space and frequencies, and to build a fine-grained shadowing map which can be used to tune the accuracy of propagation model used by GLDB. A novel distributed mobility algorithm is described for the sensing coverage of the scenario, and an aggregation mechanism for the map creation is illustrated. Simulation results confirm the effectiveness of our scheme in terms of TVWS detection accuracy and scenario coverage issues.

6. The Emergency Direct Mobile App: Safety Message Dissemination over a Multi-Group Network of Smartphones using Wi-Fi Direct

   Marco Di Felice, Luca Bedogni, and Luciano Bononi

   In Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless Access, MobiWac 2016, Malta, November 13-17, 2016, May 2016

   Abs

   The “City of Kerpen” is a rural administration unit of eleven villages near Cologne, Germany. With its 64,000 inhabitants, this region has been strongly dominated by surface mining of brown coal and the generation of electricity burning this fossil energy source in huge power plants. This is going to change dramatically. After having founded municipal utilities which own the majority of the power and energy distribution grid of the city area, the City of Kerpen plans and operates the fast transition towards flexible and decentralized generation, storage and consumption of electric, chemical and thermal energy in strong conjunction with smart traffic management. This is done in several master plans which cover built-up residential estates, but especially development areas of residential buildings as well as commercial zones. Also areas covered by infrastructure such like highways or railway routes are included in that they provide surfaces easily available for large-scale photovoltaic and/or wind power generation. This paper introduces the concepts for which the outlines plus some detailed concepts have been developed during the last years, an important step being the mentioned foundation of municipal utilities. The authors discuss the present status of this development in the light of recent R&D results which focus on the efficient management between large scale EV traffic and decentralized electric energy generation and storage on all scales. An outlook is given how the activities of the City of Kerpen could be used to upscale these R&D results from lab scale to real-world size.

7. From brown coal to a rural energy landscape - Orchestration of storage and electric mobility to foster decentralized energy management

   Alfredo D’Elia, Marco Di Felice, Luca Bedogni, and 3 more authors

   In 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow, RTSI 2016, Bologna, Italy, September 7-9, 2016, May 2016

   Abs DOI

   The Internet of things is foreseen as one of the next imminent Internet revolutions, as many devices will seamlessly communicate together to provide new and exciting services to the end users. One of the challenges that the IoT has to face is about both the heterogeneity of the data available and the heterogeneity of the communication. In this paper we focus on the former, by presenting an architecture able to integrate data coming from different sources, including custom made deployments and government data. New services can be deployed directly by the end users, using reliable or unreliable data sources, and new processed data can be gathered by these services and used by others.

8. On the integration of heterogeneous data sources for the collaborative Internet of Things

   Federico Montori, Luca Bedogni, and Luciano Bononi

   In 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow, RTSI 2016, Bologna, Italy, September 7-9, 2016, May 2016

   Abs DOI

   In the recent years the Smart City paradigm has gained interest worldwide. Services are built on top of data sensed in the city and then analyzed in order to enhance people’s quality of life. Nowadays users are also able to participate in such a data gathering, mostly thanks to a reduction in the cost of the sensing devices. Moreover, smartphones encompass many useful sensors and can be leveraged to obtain data by end users on the move, within the scope of mobile crowdsensing. In this paper, we propose SenSquare, a mobile crowdsensing architecture for Smart Cities, built to embrace both data availability and devices heterogeneity. SenSquare also offers the possibility for stakeholders to reward users sharing their data. Finally, we compare our proposal against a non-smart ideal architecture, showing the benefits and the advantages of a smart architecture such as SenSquare.

9. SenSquare: A mobile crowdsensing architecture for smart cities

   Federico Montori, Luca Bedogni, Alain Di Chiappari, and 1 more author

   In 3rd IEEE World Forum on Internet of Things, WF-IoT 2016, Reston, VA, USA, December 12-14, 2016, May 2016

   Abs DOI

   The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches.

10. Workshop message: CORAL 2016

    Marco Di Felice, Yue Frank Gao, and Luca Bedogni

    In 17th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2016, Coimbra, Portugal, June 21-24, 2016, May 2016

    Abs DOI

    In the aftermath of a natural calamity, relief operations can be hindered by damages to the terrestrial infrastructures (e.g. cellular base stations) that might lead to the disruption of wireless communication services. As a result, network partitions made up of isolated End-User (EU) devices, heterogeneous in terms of wireless access technologies and transmitting frequency bands, can occur within the scenario. In this paper, we address the problem of how to deploy a temporary and dynamic wireless network in order to quickly re-establish the end-to-end connectivity among isolated devices in a post-disaster environment. To this purpose, we propose the utilization of Repairing Units (RUs), consisting of Unmanned Ground Vehicles (UGVs) equipped with multiple Cognitive Radio (CR) devices; swarms of RUs are able to self-organize into a Repairing Mesh Network (RMN) that connects the isolated EU devices. Three main contributions are provided in this paper. First, we address the theoretical problem of determining the optimal deployment of the RMN (in terms of position and channel allocation on each RU), so that the number of connected EU devices is maximized, given a constrained number of available RUs. We further divide the deployment problem into a multi-channel spatial coverage and mesh connectivity problems, and we provide an approximated (optimal) solution. Second, we propose a distributed algorithm-based on the virtual spring force model-through which the RUs are able to explore the scenario in terms of space/frequency, and to create the RMN. Third, we evaluate connectivity and adaptiveness of the distributed solution through extensive Omnet++ simulations and a small scale test-bed. Simulation results show that the distributed RMN deployment algorithm provides performance close to the approximated solution in terms of covered EU devices. Experimental results demonstrate the ability of the distributed virtual spring model to adapt to dynamic propagation conditions, in order to maximize the quality of the wireless links of the RMN.

11. Context-aware Android applications through transportation mode detection techniques

    Luca Bedogni, Marco Di Felice, and Luciano Bononi

    Wirel. Commun. Mob. Comput., May 2016

    Abs DOI

    Today’s applications and providers are very interested in knowing the social aspects of users in order to customize the services they provide and to be more effective. Among the others, the most frequented places and the paths to reach them are information that turns out to be very useful to define users’ habits. The most exploited means to acquire positions and paths is the GPS sensor, however it has been shown how leveraging inertial data from installed sensors can lead to path identification. In this work, we present a Computationally Efficient algorithm to Reconstruct Vehicular Traces (CERT), a novel algorithm which computes the path traveled by a vehicle using accelerometer and magnetometer data. We show that by analyzing data obtained through the accelerometer and the magnetometer in vehicular scenarios, CERT achieves almost perfect identification for medium and small sized cities. Moreover, we show that the longer the path, the easier it is to recognize it. We also present results characterizing the privacy risks depending on the area of the world, since, as we show, urban dynamics play a key role in the path detection.

2015

1. Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

   Alfredo D’Elia, Fabio Viola, Federico Montori, and 9 more authors

   IEEE Access, May 2015

   Abs DOI

   Spontaneous wireless networks constructed out of mobile end-user devices (e.g. smartphones or tablets) are currently receiving considerable interest as they enable a wide range of novel, highly pervasive and user-centric network services and applications. In this paper, we focus on emergency-related scenarios, and we investigate the potential of spontaneous networks for providing Internet connectivity over the emergency area through the sharing of resources owned by the end-user devices. Novel and extremely flexible network deployment strategies are required in order to cope with the user mobility, the limited communication capabilities of wireless devices, and the intrinsic dynamics of traffic loads and QoS requirements. To this purpose, we propose here a novel approach toward the deployment of spontaneous networks composed by a new generation of wireless devices - called Stem Nodes (SNs) - to emphasize their ability to cover multiple network roles (e.g. gateway, router). The self-organization of the spontaneous network is then achieved through the local reconfiguration of each SN. Two complementary research contributions are provided. First, we describe the software architecture of a SN (which can be implemented on top of existing end-user devices), and we detail how a SN can manage its role set, eventually extending it through cooperation with other SNs. Second, we propose distributed algorithms, based on swarm intelligence principles, through which each SN can autonomously select its role, and self-elect to gateway or router, so that end-to-end performance are maximized while the lifetime of the spontaneous emergency network is prolonged. The ability of the proposed algorithm to guarantee adaptive and self-organizing network behaviors is demonstrated through extensive Omnet++ simulations, and through a prototype implementation of the SN architecture on a real testbed.

2. Connectivity recovery in post-disaster scenarios through Cognitive Radio swarms

   Angelo Trotta, Marco Di Felice, Luca Bedogni, and 2 more authors

   Comput. Networks, May 2015

   Abs DOI

   According to several recent studies, the overhead caused by charging operations to the users’ daily activities constitutes one of the main issues discouraging the purchasing of an Electric Vehicle (EV). At present, multiple factors contribute to such overhead, including the limited EV range, the duration of the charging phase, and the non-uniform coverage of the Equipment Vehicle Service Stations (EVSSs) in most areas of the world. Although the situation is going to improve in the long term thanks to the technological advances of the EVs and of the charging infrastructures, ICT-based solutions are needed to minimize the overhead in the short term. To this aim, in this paper we propose the WhatIF application, a software that allows the planning and simulation of EV-related scenarios. Through a mobile client, the users of our system can register their daily journeys, including driving activities and planned stops. A back-end module allows verifying the feasibility of a journey with an EV, scheduling the (eventual) recharging operations during the planned stops. A feasibility energy-optimal algorithm minimizing the overall charged energy is proposed. The performance of the WhatIF application has been tested over a large-scale simulated EV scenario (i.e. the Italian Emilia-Romagna region), considering realistic road topology, EVSSs locations, EV battery models and mobility patterns. Simulation results indicate that 76% of the EVs are able to complete their journeys when scheduling charging operations during the stops, hence confirming the effectiveness of the WhatIF application in mitigating the overhead of EV mobility.

3. STEM-NET: How to deploy a self-organizing network of mobile end-user devices for emergency communication

   Gianluca Aloi, Luca Bedogni, Luciano Bononi, and 7 more authors

   Comput. Commun., May 2015

   Abs DOI

   Nowadays, the convergence of Internet of Things (IoT) networking and mobile applications is favoring the deployment of novel and advanced smart parking systems through which users can be informed in real-time about the presence of vacant parking spots close to their destinations. In this paper, we provide an example of such opportunity, by describing Park Here!, a novel mobile application that aims at mitigating the overhead caused by parking spot seeking operations in urban areas. Our solution targets common city environments, where no per-spot sensors are available, and there is no remote service allowing the reservation in-advance of a parking spot. For this scenario, we propose a novel algorithm for the automatic detection of parking actions performed by the user, through the analysis of smartphone embedded sensors’ (accelerometer/gyroscope), and of the Bluetooth connectivity. Once a parking event has been detected, an adaptive strategy allows disseminating the information over the target scenario, using a combination of Internet connection to a remote server, and Device-to-Device (D2D) connections over WiFi Direct links. Preliminary experiments demonstrate the accuracy of the proposed algorithm in correctly identifying parking events in an automatic way, and hence in notifying information to other potentially interested users.

4. The Bologna Ringway Dataset: Improving Road Network Conversion in SUMO and Validating Urban Mobility via Navigation Services

   Luca Bedogni, Marco Gramaglia, Andrea Vesco, and 3 more authors

   IEEE Trans. Veh. Technol., May 2015

   Abs DOI

   Spectrum scarcity demands for additional bandwidth where new services can be deployed on. However, today’s spectrum allocation leaves almost no bands unallocated. Thus, Cognitive Radio has been studied to bring relief to the lack of spectrum, moving towards a more efficient and dynamic spectrum access. In this domain TV White Space have been proposed as a possible solution to bring new, valuable spectrum for opportunistic services. However, their availability is quite low in highly populated areas, and thus their viability is limited. This is mainly because the availability of TV White Space is typically considered at the rooftop, through two-dimensional propagation models which do not account for possible spectrum re-utilization policies within a building, or in a small-scale area. In this paper, we show that much more communication opportunities can be found when we consider also the third-dimension, i.e. the height from the terrain, and novel per-floor allocation policies. We propose three main contributions in this paper. First, we describe an analytical model through which we derive the number of available spectrum resources for indoor secondary networks, considering PU protection policies in the same building, and in surrounding buildings. Second, we estimate the number of TV Gray Space (TVGS) over realistic scenarios in candidate cities, considering realistic street topology and buildings locations, and we show that this value can be much higher than what reported in the spectrum database. Finally, we investigate co-existence of secondary networks on TVWS, when novel per-floor spectrum sharing models are used.

5. WhatIF Application: Moving Electrically without an Electric Vehicle

   Luca Bedogni, Luciano Bononi, Marco Di Felice, and 2 more authors

   In Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2015, Cancun, Mexico, November 2-6, 2015, May 2015

   Abs DOI

   In the recent years, wireless communication experienced a huge growth, and nowadays many services are built on top of it. Much of the user demands comes from indoor environments, in which obstructions like walls and floors decrease the received signal to levels not suitable for reliable and high speed communication. Recently, national regulators worldwide started to investigate the usage of TV bands, thanks to the switch from analog to digital TV. These regulations gave birth to different wireless standards to make use of this new opportunistic spectrum. In this paper, we show how different 802.11 standard behave in indoor environment. Namely, we analyze IEEE 802.11n networks in the 2.4 GHz band, and IEEE 802.11af in the TV band, by means of theoretical analisys and simulations. Then, we design an algorithm that leverages the use of the different IEEE 802.11 amendments, by monitoring the achievable data rates with respect to the packet-error-rate, and provide simulation results on its performance on different modeled scenarios.

6. Park Here! a smart parking system based on smartphones’ embedded sensors and short range Communication Technologies

   Rosario Salpietro, Luca Bedogni, Marco Di Felice, and 1 more author

   In 2nd IEEE World Forum on Internet of Things, WF-IoT 2015, Milan, Italy, December 14-16, 2015, May 2015

   Abs DOI

   Nowadays, the convergence of Internet of Things (IoT) networking and mobile applications is favoring the deployment of novel and advanced smart parking systems through which users can be informed in real-time about the presence of vacant parking spots close to their destinations. In this paper, we provide an example of such opportunity, by describing Park Here!, a novel mobile application that aims at mitigating the overhead caused by parking spot seeking operations in urban areas. Our solution targets common city environments, where no per-spot sensors are available, and there is no remote service allowing the reservation in-advance of a parking spot. For this scenario, we propose a novel algorithm for the automatic detection of parking actions performed by the user, through the analysis of smartphone embedded sensors’ (accelerometer/gyroscope), and of the Bluetooth connectivity. Once a parking event has been detected, an adaptive strategy allows disseminating the information over the target scenario, using a combination of Internet connection to a remote server, and Device-to-Device (D2D) connections over WiFi Direct links. Preliminary experiments demonstrate the accuracy of the proposed algorithm in correctly identifying parking events in an automatic way, and hence in notifying information to other potentially interested users.

7. On 3-dimensional spectrum sharing for TV white and Gray Space networks

   Luca Bedogni, Angelo Trotta, and Marco Di Felice

   In 16th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2015, Boston, MA, USA, June 14-17, 2015, May 2015

   Abs DOI

   Spectrum scarcity demands for additional bandwidth where new services can be deployed on. However, today’s spectrum allocation leaves almost no bands unallocated. Thus, Cognitive Radio has been studied to bring relief to the lack of spectrum, moving towards a more efficient and dynamic spectrum access. In this domain TV White Space have been proposed as a possible solution to bring new, valuable spectrum for opportunistic services. However, their availability is quite low in highly populated areas, and thus their viability is limited. This is mainly because the availability of TV White Space is typically considered at the rooftop, through two-dimensional propagation models which do not account for possible spectrum re-utilization policies within a building, or in a small-scale area. In this paper, we show that much more communication opportunities can be found when we consider also the third-dimension, i.e. the height from the terrain, and novel per-floor allocation policies. We propose three main contributions in this paper. First, we describe an analytical model through which we derive the number of available spectrum resources for indoor secondary networks, considering PU protection policies in the same building, and in surrounding buildings. Second, we estimate the number of TV Gray Space (TVGS) over realistic scenarios in candidate cities, considering realistic street topology and buildings locations, and we show that this value can be much higher than what reported in the spectrum database. Finally, we investigate co-existence of secondary networks on TVWS, when novel per-floor spectrum sharing models are used.

2014

1. Cognitive modulation and coding scheme adaptation for 802.11n and 802.11af networks

   Luca Bedogni, Marco Di Felice, Fabio Malabocchia, and 1 more author

   In 2014 IEEE GLOBECOM Workshops, Austin, TX, USA, December 8-12, 2014, May 2014

   Abs DOI

   The use of wireless technology to communicate has spread to a variety of devices. Traditional solutions based on the well known IEEE 802.11 struggle to cope with a large number of contending nodes. The need to improve throughput and meet QoS requirements has driven the quest for more elaborated channel access mechanisms. The main contribution of this work is to propose a fair and fast channel access resolution protocol, named CRP (Collision Resolution Protocol). CRP explores the use of pulse and tone signaling to select the appropriate transmitting node among a number of contending stations, allowing for frame transmissions without collision. We perform extensive simulations, and the results show that CRP is capable to grant channel access in less than 1/600 of the time of other similar mechanisms while being able to deliver over 32% more transmissions per second. Furthermore, the CRP provides lower channel access latency and a fair resource allocation, which makes it suitable to support applications with demanding QoS.

2. A Collision-Free Contention Protocol Based on Pulse/Tone Signals

   Marcos F. Caetano, Jacir Luiz Bordim, Luca Bedogni, and 1 more author

   In Second International Symposium on Computing and Networking, CANDAR 2014, Shizuoka, Japan, December 10-12, 2014, May 2014

   Abs DOI

   Electric Vehicles (EVs) represent one of the most promising solutions toward sustainable transportation systems. However, some aspects of EV-based mobility pose challenges for a larger market uptake. Among the others, the overhead of charging operations (e.g. Long recharge time), and the lack of accurate information about availability of EV supply stations (EVSSs) while being on board of an EV are perceived by customers as important limitations, and determine a low user acceptance. To tackle these issues, additional assistance must be provided to EV drivers, through the utilization of ICT-based solutions. In this paper, we describe the implementation of a mobile Android application, which has been deployed within the EU Internet of Energy (IoE) project, with the goal of supporting a larger uptake of EV-based mobility. The application provides full assistance to EV drivers, through functionalities of battery monitoring, dynamic range prediction, and EVSS discovery along the way. Moreover, it supports the IoE semantic architecture, and allows EV drivers reserving a charging slot based on their preferences, and on current availability of EVSSs. The user acceptance of the application has been tested through a questionnaire. Test results confirm the importance of charging reservation mechanisms to mitigate EV driver anxiety problems.

3. Driving without anxiety: A route planner service with range prediction for the electric vehicles

   Luca Bedogni, Luciano Bononi, Alfredo D’Elia, and 3 more authors

   In International Conference on Connected Vehicles and Expo, ICCVE 2014, Vienna, Austria, November 3-7, 2014, May 2014

   Abs DOI

   With an increasing demand for monitoring energy consumption at granularity levels down to single household appliances, it is necessary to develop new means to collect sensor measurements in a robust and cost-efficient manner. The smart grid paradigm foresees using wireless links for data transfer, albeit no dedicated spectrum bands have been designated for this purpose. In this paper we study the feasibility of opportunistic spectrum access for smart grids, and focus on underlay spectrum sharing over occupied TV channels. These frequency bands, which are commonly denoted as TV gray spaces, provide superior propagation characteristics, but are locally used by high-power (mostly DTV) broadcasting transmitters. For selected reference geometries of intra-meter and meter-to-operator communications, we study the smart meter performance (in terms of achievable throughput and transmission range), and the necessary power limits. We compare our results from a small-scale measurement campaigns against existing wireless technologies for low-power communications in other adjacent bands. Our results show that wall shielding and fading in indoor to outdoor propagation channels sufficiently protects the primary system from the interference introduced by gray space meter-to-meter communications, but that the required transmit powers to send operations data from indoor meters to outdoor collection point severely limit the applicability of TV gray spaces for such network topologies.

4. A Mobile Application to Assist Electric Vehicles’ Drivers with Charging Services

   Luca Bedogni, Luciano Bononi, Alfredo D’Elia, and 3 more authors

   In Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014, Oxford, United Kingdom, September 10-12, 2014, May 2014

   Abs DOI

   Nowadays several mobile applications connect to the internet through 2G/3G/LTE, which are becoming more crowded. Cognitive wireless networks have been proposed as a possible solution to supply additional bandwidth, and more recently TV White Spaces (TVWS) have been investigated as one candidate. TVWS devices should contact a remote spectrum database, which will reply with the channels available to use. It is not specified how devices should contact the remote spectrum database, so in this work we focus on the usage of a cellular connection, where however the number of the queries could rapidly grow and occupy considerable bandwidth. In this paper we present the idea of Femto-Databases, i.e. devices which act as distributed mobile databases able to satisfy the spectrum requests by opportunistic devices. Extensive simulations through the Omnet++ platform show that our approach can effectively reduce the load on the cellular infrastructure, and improve the latency of the query communication to the remote spectrum database.

5. Self-organizing aerial mesh networks for emergency communication

   Marco Di Felice, Angelo Trotta, Luca Bedogni, and 2 more authors

   In 25th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communication, PIMRC 2014, Washington DC, USA, September 2-5, 2014, May 2014

   Abs DOI

   The spectrum scarcity is a known problem for a multitude of services. Several bands have been licensed, and nowadays it is difficult to find unused spectrum. Cognitive radio networks have been proposed as a possible solution to contrast the experienced spectrum scarcity. One case of particular interest come from the scarce utilization of TV frequencies, which form the so-called TV White Spaces. In this paper we investigate the utilization of occupied frequencies by secondary devices for indoor communication. We conduct spectrum measurements to quantify the availability of spectrum, and study how indoor communications could impact the DTV receiver. We show that this portions of spectrum, called gray spaces, can be utilized under certain circumstances, for example in highly populated areas, which is the scenario in which it is harder to find TV White Spaces. Simulation studies show the impact gray spaces can have on the available spectrum for opportunistic use.

6. Smart meters with TV gray spaces connectivity: A feasibility study for two reference network topologies

   Luca Bedogni, Andreas Achtzehn, Marina Petrova, and 1 more author

   In Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2014, Singapore, June 30 - July 3, 2014, May 2014

   Abs DOI

   Spontaneous networks among end-user devices (e.g, smartphones, tablets) can guarantee emergency communication in post-disaster scenarios where the original infrastructure has been partially damaged by the occurrence of unpredictable or catastrophic events. However, the heterogeneity of devices and wireless access technologies poses important challenges on the network deployment and management. In this paper, we propose the STEM-Net architecture as a viable network model to handle the devices’ heterogeneity and to enable spontaneous networking functionalities in post-disaster scenarios. In STEM· Net, the wireless devices - called Stem Nodes (SN) - are able to adapt their transmitting configurations, cover different roles (e.g. router, bridge, etc) according to the system needs and evolve their functionalities through cooperation with other nodes. Here, we provide a proof-of-concept of the principles of nodes’ mutation and evolution, by discussing how heterogeneous end-user devices provided with SN capabilities can dynamically self-organize into multi-hop networks, and share the Internet access by switching among three roles: stub, transit and gateway SNs. A bio-inspired distributed gateway selection mode is proposed to allow each SN device to decide its current role, based on the system needs and on the individual hardware characteristics and resources (e.g residual energy or queue occupation). The simulation analysis conducted with the Omnet++ tool demonstrates the effectiveness of the STEM-Net framework in prolonging the network lifetime while providing adequate bandwidth for emergency communication to the end-users devices.

7. Distributed Mobile Femto-Databases for Cognitive Access to TV White Spaces

   Luca Bedogni, Marco Di Felice, Angelo Trotta, and 1 more author

   In IEEE 80th Vehicular Technology Conference, VTC Fall 2014, Vancouver, BC, Canada, September 14-17, 2014, May 2014

   Abs DOI

   Machine-to-Machine communications is envisioned to become one of the fundamental pillars of the future Internet of Things paradigm, enabling platoons of devices to be seamlessly connected and to cooperate over smart spaces. Among the possible application scenarios, smart metering represents an already existing technology that might take benefit from the capability of autonomous configuration and setup of M2M networks. At present, smart meters communicate over the 2G/3G network, however the utilization of the cellular technology poses several problems, such as low coverage and spectrum shortage over dense areas. To overcome these issues, in this paper we investigate the application of cognitive radio principles over TV White Spaces to M2M communication for the smart metering scenario. Following the recent regulations of FCC and Ofcom, that foresees the presence of a spectrum database for TV white spaces detection, we study the trade-off between protection of licensees and energy consumption in a cluster of smart meters. We provide three novel research contributions: (i) an analytical model to estimate the lifetime of a cluster of smart meters; (ii) centralized and distributed algorithms to determine the schedule operations of Master/Slave devices foreseen by the spectrum regulations; (iii) performance evaluation of the proposed framework through extensive Omnet++ simulations.

8. Indoor communication over TV gray spaces based on spectrum measurements

   Luca Bedogni, Marco Di Felice, Fabio Malabocchia, and 1 more author

   In IEEE Wireless Communications and Networking Conference, WCNC 2014, Istanbul, Turkey, April 6-9, 2014, May 2014

   Abs DOI

   In this paper, we address the problem of re-establishing the network connectivity in post-disaster scenarios, where the original wireless infrastructure has been partitioned into multiple network fragments (called islands), operating on different frequencies. To this purpose, we propose the utilization of swarms of dedicated repairing units, called Stem-Nodes (SNs). SNs are provided with Cognitive Radio (CR) and self-positioning capabilities, in order to offer maximum reconfigurability in terms of mobility and wireless technologies supported. Moreover, swarms of SNs can self-organize into STEM-Mesh structure, that works as a dynamic backbone to connect heterogeneous islands using different technologies (e.g. Wi-Fi, Wi-MAX, etc). In this paper, we present three contributions pertaining to STEM-Mesh: (i) we describe a distributed motion control scheme (based on virtual springs approach) that enables SNs to self-organize into dynamic STEM-Mesh structures, (ii) we introduce a discovery scheme, through which SNs can explore the scenario in both spatial and frequency domains, and possibly connect the islands to the STEM-Mesh backbone and (iii) we validate the correctness of the proposed scheme, by verifying the optimal placements of the SNs composing the STEM-Mesh on a simplified scenario (e.g. chain topology). Finally, we evaluate through Omnet++ simulations the ability of STEM-Mesh to maximally re-establish connectivity on partitioned network scenarios.

9. STEM-Net: an evolutionary network architecture for smart and sustainable cities

   Gianluca Aloi, Luca Bedogni, Marco Di Felice, and 7 more authors

   Trans. Emerg. Telecommun. Technol., May 2014

   Abs DOI

   The cornerstone of Industry 5.0 is the human, its well-being, development, and creativity at the center of the production process. To achieve this, recording emotional, psychological, physical, and cognitive states efficiently in real-time is crucial. In particular, monitoring a complex psychophysiological state such as stress requires obtaining information from reliable biological signals such as electrocardiogram (ECG), galvanic skin response (GSR), electroencephalogram (EEG), and facial expressions. In this study, we introduce a new dataset, SenseCobotFusion, which collects stress-related metrics derived from physiological signals recorded from operators engaged in Human-Robot Collaboration (HRC) tasks. Labeled with the subjective operator rating obtained with the NASA-TLX questionnaire tool, SenseCobotFusion is a new dataset available to the research community focused on stress and workload detection. SenseCobotFusion is structured to be flexible and compatible with other existing datasets and potential experimental scenarios. To achieve this, a thorough dataset encompassing all metrics and specific sub-datasets for each signal type was developed, enabling seamless adaptation to user needs. As a tentative example of the potential of SenseCobotFusion, machine learning models were trained on each of these datasets. The results align with findings in the literature: the stress response is highly subjective and influenced by numerous factors, both dependent and independent of the operator. Additionally, the signal processing pipeline codes used to extract the metrics of interest, specific for GSR, EEG, ECG, and Emotions data, was also provided, which can be used as a guideline to extract stress-related metrics from SenseCobot and similar datasets.

2013

1. Group communication on highways: An evaluation study of geocast protocols and applications

   Marco Di Felice, Luca Bedogni, and Luciano Bononi

   Ad Hoc Networks, May 2013

   Abs DOI

   Network interoperability and self-organization constitute important communication requirements in disaster recovery scenarios. Here, the original communication infrastructure might be partially or completely damaged, and the whole network might be partitioned into segments (called islands in the following) that might operate on different frequencies/wireless technologies. In this paper, we investigate techniques to maximally re-establish the connectivity among heterogeneous islands through the utilization of specialized repairing units called Stem Nodes (SNs). A SN combines spectrum reconfigurability (offered by the Software Defined Radio technology) with self-positioning and dynamic routing functionalities, and thus it is able to replace damaged components of the original infrastructure. Moreover, sets of SNs can self-organize into multi-hop mesh structures connecting heterogeneous islands. We study the problem of determining the optimal deployment of SNs so that the number of connected devices of the original network is maximized. Given the NP-hardness of the problem, we propose approximated solutions with reduced computational complexity. We then compare the centralized solution with a distributed algorithm (based on virtual springs approach) that enables SNs to explore the environment in both space/frequency domains, and to self-organize into virtual mesh structures. Simulation results confirm the effectiveness of the distributed algorithm to maximally re-establish the network connectivity even on large-scale scenarios.

2. Smartphones like stem cells: Cooperation and evolution for emergency communication in post-disaster scenarios

   Marco Di Felice, Luca Bedogni, Angelo Trotta, and 6 more authors

   In First International Black Sea Conference on Communications and Networking, BlackSeaCom 2013, Batumi, Georgia, July 3-5, 2013, May 2013

   Abs DOI

   Spontaneous networks among end-user devices (e.g, smartphones, tablets) can guarantee emergency communication in post-disaster scenarios where the original infrastructure has been partially damaged by the occurrence of unpredictable or catastrophic events. However, the heterogeneity of devices and wireless access technologies poses important challenges on the network deployment and management. In this paper, we propose the STEM-Net architecture as a viable network model to handle the devices’ heterogeneity and to enable spontaneous networking functionalities in post-disaster scenarios. In STEM· Net, the wireless devices - called Stem Nodes (SN) - are able to adapt their transmitting configurations, cover different roles (e.g. router, bridge, etc) according to the system needs and evolve their functionalities through cooperation with other nodes. Here, we provide a proof-of-concept of the principles of nodes’ mutation and evolution, by discussing how heterogeneous end-user devices provided with SN capabilities can dynamically self-organize into multi-hop networks, and share the Internet access by switching among three roles: stub, transit and gateway SNs. A bio-inspired distributed gateway selection mode is proposed to allow each SN device to decide its current role, based on the system needs and on the individual hardware characteristics and resources (e.g residual energy or queue occupation). The simulation analysis conducted with the Omnet++ tool demonstrates the effectiveness of the STEM-Net framework in prolonging the network lifetime while providing adequate bandwidth for emergency communication to the end-users devices.

3. Machine-to-Machine Communication over TV White Spaces for Smart Metering Applications

   Luca Bedogni, Angelo Trotta, Marco Di Felice, and 1 more author

   In 22nd International Conference on Computer Communication and Networks, ICCCN 2013, Nassau, Bahamas, July 30 - Aug. 2, 2013, May 2013

   Abs DOI

   Machine-to-Machine communications is envisioned to become one of the fundamental pillars of the future Internet of Things paradigm, enabling platoons of devices to be seamlessly connected and to cooperate over smart spaces. Among the possible application scenarios, smart metering represents an already existing technology that might take benefit from the capability of autonomous configuration and setup of M2M networks. At present, smart meters communicate over the 2G/3G network, however the utilization of the cellular technology poses several problems, such as low coverage and spectrum shortage over dense areas. To overcome these issues, in this paper we investigate the application of cognitive radio principles over TV White Spaces to M2M communication for the smart metering scenario. Following the recent regulations of FCC and Ofcom, that foresees the presence of a spectrum database for TV white spaces detection, we study the trade-off between protection of licensees and energy consumption in a cluster of smart meters. We provide three novel research contributions: (i) an analytical model to estimate the lifetime of a cluster of smart meters; (ii) centralized and distributed algorithms to determine the schedule operations of Master/Slave devices foreseen by the spectrum regulations; (iii) performance evaluation of the proposed framework through extensive Omnet++ simulations.

4. STEM-mesh: Self-organizing mobile cognitive radio network for disaster recovery operations

   Marco Di Felice, Angelo Trotta, Luca Bedogni, and 5 more authors

   In 2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013, Sardinia, Italy, July 1-5, 2013, May 2013

   Abs DOI

   In this paper, we address the problem of re-establishing the network connectivity in post-disaster scenarios, where the original wireless infrastructure has been partitioned into multiple network fragments (called islands), operating on different frequencies. To this purpose, we propose the utilization of swarms of dedicated repairing units, called Stem-Nodes (SNs). SNs are provided with Cognitive Radio (CR) and self-positioning capabilities, in order to offer maximum reconfigurability in terms of mobility and wireless technologies supported. Moreover, swarms of SNs can self-organize into STEM-Mesh structure, that works as a dynamic backbone to connect heterogeneous islands using different technologies (e.g. Wi-Fi, Wi-MAX, etc). In this paper, we present three contributions pertaining to STEM-Mesh: (i) we describe a distributed motion control scheme (based on virtual springs approach) that enables SNs to self-organize into dynamic STEM-Mesh structures, (ii) we introduce a discovery scheme, through which SNs can explore the scenario in both spatial and frequency domains, and possibly connect the islands to the STEM-Mesh backbone and (iii) we validate the correctness of the proposed scheme, by verifying the optimal placements of the SNs composing the STEM-Mesh on a simplified scenario (e.g. chain topology). Finally, we evaluate through Omnet++ simulations the ability of STEM-Mesh to maximally re-establish connectivity on partitioned network scenarios.

5. Re-establishing network connectivity in post-disaster scenarios through mobile cognitive radio networks

   Angelo Trotta, Marco Di Felice, Luca Bedogni, and 1 more author

   In The 12th Annual Mediterranean Ad Hoc Networking Workshop, MED-HOC-NET 2013, Ajaccio, France, June 24-26, 2013, May 2013

   Abs DOI

   This paper presents a process calculus specifically designed to model systems based on the Internet of Things paradigm. We define a formal syntax and semantics for the calculus, and show how it can be used to reason about relevant examples. We also define two notions of bisimilarity, one capturing the behavior seen by the end user of the system, and one allowing compositional reasoning.

6. Internet of things: a process calculus approach

   Ivan Lanese, Luca Bedogni, and Marco Di Felice

   In Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC ’13, Coimbra, Portugal, March 18-22, 2013, May 2013

   Abs DOI

   The Internet of Energy (IoE) for Electric Mobility is an European research project that aims at deploying a communication infrastructure to facilitate and support the operations of Electric Vehicles (EVs). In this paper, we present three research contributions of IoE. First, we describe a software architecture to support the deployment of mobile and smart services over an Electric Mobility (EM) scenario. The proposed architecture relies on an ontology-based data representation, on a shared repository of information (Service Information Broker), and on software modules (called Knowledge Processors -KPs) for standardized data access/management. As a result, information sharing among the different stakeholders of the EM scenario (i.e. EVs, EVSEs, City Services, etc) is enabled, and the interoperability of smart services offered by heterogeneous providers is guaranteed by the common ontology. Second, we rely on the proposed architecture to develop a remote charging reservation system, that runs on top of mobile smarthphones, and allows drivers to monitor the current state-of-charge of their EV, and to reserve a charging slot at a specific EVSE. Finally, we validate our architecture through a benchmark framework, that supports the embedding of mobile EV applications and of real KPs into a simulated vehicular scenario, including realistic traffic, wireless communication and battery models. Evaluation results confirm the scalability of our architecture, and the ability to support EVs charging operations on a large-scale scenario (i.e. the downtown of Bologna).

7. An interoperable architecture for mobile smart services over the internet of energy

   Luca Bedogni, Luciano Bononi, Marco Di Felice, and 8 more authors

   In IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks", WoWMoM 2013, Madrid, Spain, June 4-7, 2013, May 2013

   Abs DOI

   Recently, the IEEE 1609.4 protocol has been defined to enable multi-channel operations in a vehicular environment, and to guarantee interference-free co-existence of safety-related and non-safety related applications in the same network. To meet these goals, the protocol assumes strict time synchronization among vehicles, and time/frequency separation in the Dedicated Short Range Communication (DSRC) band. However, recent studies have demonstrated that this approach might not be suitable for safety-related broadcast applications with strict Quality-of-Service (QoS) requirements. In this paper, we investigate the potentials of Cognitive Radio (CR) technology to enhance the delivery ratio of safety-related broadcast applications in a multi-channel vehicular scenario. In this research field, we propose three novel contributions: (i) we introduce an analytical model to study the delivery ratio of broadcast applications in IEEE 802.11p/1609.4 multi-channel vehicular networks, and the impact of MAC and PHY parameters on the system performance; (ii) we propose a framework to jointly decide the optimal values of the Contention Window size (CW) at the MAC layer and of the Control CHannel (CCH) bandwidth at PHY layer, so that each vehicle is able to transmit all its safety data during the CCH interval with a minimum MAC collision probability, and (iii) we discuss the framework implementation in a realistic vehicular scenario. Our on-demand bandwidth allocation algorithm utilizes vacant frequencies in the DSRC band to increase the bandwidth of the CCH, leveraging the spectrum agile capabilities offered by the CR technology. Simulation results confirm the effectiveness of our proposal in enhancing the delivery rate of broadcast applications under varying network scenarios and load conditions.

2012

1. DySCO: a dynamic spectrum and contention controlframework for enhanced broadcast communication invehicular networks

   Marco Di Felice, Luca Bedogni, and Luciano Bononi

   In Proceedings of the 10th ACM International Symposium on Mobility Management and Wireless Access, MOBIWAC ’12, Paphos, Cyprus, October 21-22, 2012, May 2012

   Abs DOI

   Nowadays, the increasing popularity of smartphone devices equipped with multiple sensors (e.g. accelerometer, gyroscope, etc) have opened several possibilities to the deployment of novel and exciting context-aware mobile applications. In this paper, we exploit one of this possibility, by investigating how to detect the user motion type through sensors data collected by a smartphone. Our study combines experimental and analytical contributions, and it is structured in three parts. First, we collected experimental data that demonstrate the existence of specific sensors data patterns associated to each motion type, and we propose methods for data analysis and features extraction. Second, we compare the performance of different supervised algorithms for motion type classification, and we demonstrate that jointly utilizing the multiple sensor inputs of a smartphone (i.e. the accelerometer and the gyroscope) can significantly improve the accuracy of the classifiers. At the same time, we analyze the impact of sampling parameters (e.g. the sampling rate) on the system performance, and the corresponding trade-off between classification accuracy and energy consumption of the device. Third, we integrate the motion type recognition algorithm into an Android application, that allows to associate a specific smartphone configuration to each detected motion type, and to provide this information at system-level to other context-aware Android applications. Experimental results demonstrate the ability of our application in detecting the user’s motion type with high accuracy, and in mitigating the classification errors caused by random data fluctuations.

2. By train or by car? Detecting the user’s motion type through smartphone sensors data

   Luca Bedogni, Marco Di Felice, and Luciano Bononi

   In Proceedings of the IFIP Wireless Days Conference 2012, Ireland, November 21-23, 2012, May 2012

   Abs DOI

   Fast delivery and high reliability are two of the main requirements which must be guaranteed by multi-hop broadcast protocols of alert messages in VANETs. Most of the existing schemes work by selecting the optimal forwarder vehicle at each hop through a distributed contention phase, thus introducing an additional delay in the forwarding process. An alternative approach is to identify the optimal set of relay vehicles before the actual dissemination of the alert messages, by creating and maintaining a virtual backbone of vehicles inside the VANET. For this purpose, we propose here an extended version of the Dynamic Backbone-Assisted MAC (DBA-MAC) scheme which supports fast and efficient multi-hop broadcast communication in VANETs. The DBA-MAC scheme comprises two main components: (i) a distributed clustering scheme, which builds a virtual backbone of vehicles, and a (ii) fast multihop forwarding scheme, which provides contention-free forwarding of the alert messages among the backbone vehicles. Compared to our previous works on DBA-MAC, we proposes here additional metrics for backbone creation which account for the channel receiver characteristics of each vehicle, through the estimation of the Link Budget (LB) between communicating vehicles. Moreover, we show through analytical and simulation results that the performance of the DBA-MAC in terms of delivery delay can be bounded between those of a static backbone with nodes placed at the optimal distance and those of a traditional contention-based scheme, which attempts to select the farthest vehicle at each hop. Finally, through the OMNET++ and SUMO tools, we propose an extended evaluation of the DBA-MAC scheme over realistic urban scenarios by modeling the impact of vehicular mobility and shadowing effects on the protocol performance.

2011

1. Dynamic backbone for fast information delivery invehicular ad hoc networks: an evaluation study

   Marco Di Felice, Luca Bedogni, and Luciano Bononi

   In Proceedings of the 8th ACM Symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, PE-WASUN 2011, Miami Beach, Florida, USA, October 31 - November 4, 2011, May 2011

   Abs DOI

   In this article, we assess the viability of underlay sensor networks in frequencies used by an incumbent digital TV broadcasting system, that is, in the so-called TV grayspaces (TVGS). Grayspace operations are particularly interesting when other unlicensed bands are overcrowded, for example, due to high-volume WiFi operations. We simulate the operational characteristics of the recent IEEE 802.15.4m standard for low-rate wireless personal area networks to evaluate the performance degradation of an incumbent Digital Video Broadcasting - Terrestrial (DVB-T) system if a secondary network of low-power low-rate devices are co-deployed in the same frequency bands. Our results show that short sensor messages will not disrupt the DVB-T service due to the existing error-correction capabilities. Furthermore, if sufficient separation distances to primary transmitters are maintained, transmit powers are sufficient to achieve reasonable connectivity levels of the secondary network. In order to obtain realistic figures on the predicted feasibility of grayspace sensor networks, we study the deployment constraints of a hypothetical secondary network co-located with the TV broadcasting network of Germany. Our analysis shows that if we aim to support a minimum sensor-sensor distance, no universal coverage can be maintained in this country. While our quantitative results are specific to Germany, we deem them indicative for the expected results also in other potential deployments. We found that while a secondary wireless sensor network in TVGS is technically possible, the necessary constraints on operational parameters and service levels for TVGS co-existence will significantly limit its practical viability.