- Profile
- Research Interests
- Projects
- Publications
- Professional Activities
- Teaching & Students
Profile
Luca Davoli
Fixed-term Assistant Professor
(“Ricercatore a tempo determinato – Tipo A”)
Department of Engineering and Architecture
Parco Area delle Scienze, 181/A
43124 Parma – Italy
Parco Area delle Scienze, 181/A
43124 Parma – Italy
luca.davoli[at]unipr.it
Luca Davoli currently is a Fixed-term Assistant Professor at the University of Parma. He received his Dr. Ing. (Laurea) degree in Computer Engineering from the University of Parma, Italy, in 2013 with a thesis entitled “Distributed and Scalable Architecture for Service Discovery in IoT”.
In 2017, he received his Ph.D. in Information Technologies at the Department of Information Engineering of the same university with a thesis entitled “Architecture and Technologies for the Internet of Things”.
Since January 2014, he is a member of the Internet of Things Lab (IoTLab) (ex Wireless Ad-Hoc and Sensor Networks Laboratory – WASNLab) at the Department of Engineering and Architecture of the University of Parma.
He is an IEEE member and, since 2018, a GTTI member.
In 2017, he received his Ph.D. in Information Technologies at the Department of Information Engineering of the same university with a thesis entitled “Architecture and Technologies for the Internet of Things”.
Since January 2014, he is a member of the Internet of Things Lab (IoTLab) (ex Wireless Ad-Hoc and Sensor Networks Laboratory – WASNLab) at the Department of Engineering and Architecture of the University of Parma.
He is an IEEE member and, since 2018, a GTTI member.
Research Interests
Internet of Things
Software Defined Networking
Big Stream
Peer-to-Peer Networks
Software Defined Networking
Big Stream
Peer-to-Peer Networks
Projects
On-going projects
[embed_ongoing_projects category=”projects-active-ld”]
Completed projects
[embed_completed_projects category=”projects-completed-ld”]
Publications
2025
Armin Mazinani; Luca Davoli; Gianluigi Ferrari
Deep Learning Algorithms for Cryptocurrency Price Prediction: A Comparative Analysis Journal Article
In: Distributed Ledger Technologies: Research and Practice, 4 (1), pp. 1-38, 2025.
@article{madafe:2025:dlt,
title = {Deep Learning Algorithms for Cryptocurrency Price Prediction: A Comparative Analysis},
author = {Armin Mazinani and Luca Davoli and Gianluigi Ferrari},
doi = {10.1145/3699966},
year = {2025},
date = {2025-02-07},
urldate = {2025-01-01},
journal = {Distributed Ledger Technologies: Research and Practice},
volume = {4},
number = {1},
pages = {1-38},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as (R^2) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as (R^2) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.
Laura Belli; Luca Davoli; Gianluigi Ferrari
L’IoT e la trasformazione dei sistemi complessi: oltre i gateway commerciali Online
Agenda Digitale 2025, visited: 03.01.2025.
@online{bedafe:2025:agendadigmig,
title = {L’IoT e la trasformazione dei sistemi complessi: oltre i gateway commerciali},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},
url = {https://www.agendadigitale.eu/infrastrutture/liot-e-la-trasformazione-dei-sistemi-complessi-oltre-i-gateway-commerciali/},
year = {2025},
date = {2025-01-03},
urldate = {2025-01-03},
organization = {Agenda Digitale},
abstract = {L’Internet of Things sta trasformando il modo in cui viviamo e lavoriamo, connettendo dispositivi eterogenei in vari ambiti applicativi, dalla domotica all’industria, creando ecosistemi interconnessi sempre più complessi.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
L’Internet of Things sta trasformando il modo in cui viviamo e lavoriamo, connettendo dispositivi eterogenei in vari ambiti applicativi, dalla domotica all’industria, creando ecosistemi interconnessi sempre più complessi.
2024
Martina Galaverni; Giulia Oddi; Luca Preite; Laura Belli; Luca Davoli; Ilaria Marchioni; Margherita Rodolfi; Federico Solari; Deborah Beghè; Tommaso Ganino; Giuseppe Vignali; Gianluigi Ferrari
An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes Journal Article
In: Computers and Electronics in Agriculture, 229 , pp. 109660, 2024, ISSN: 0168-1699.
@article{gaother:2025:cea,
title = {An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes},
author = {Martina Galaverni and Giulia Oddi and Luca Preite and Laura Belli and Luca Davoli and Ilaria Marchioni and Margherita Rodolfi and Federico Solari and Deborah Beghè and Tommaso Ganino and Giuseppe Vignali and Gianluigi Ferrari},
doi = {10.1016/j.compag.2024.109660},
issn = {0168-1699},
year = {2024},
date = {2024-12-01},
urldate = {2025-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {229},
pages = {109660},
abstract = {The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.
Armin Mazinani; Danilo Pietro Pau; Luca Davoli; Gianluigi Ferrari
Deep Neural Quantization for Speech Detection of Parkinson Disease Inproceedings
In: 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), pp. 178-183, Milan, Italy, 2024, ISSN: 2687-6817.
@inproceedings{mapadafe:2024:rtsi,
title = {Deep Neural Quantization for Speech Detection of Parkinson Disease},
author = {Armin Mazinani and Danilo Pietro Pau and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/RTSI61910.2024.10761283},
issn = {2687-6817},
year = {2024},
date = {2024-11-26},
urldate = {2024-09-01},
booktitle = {2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)},
pages = {178-183},
address = {Milan, Italy},
abstract = {Among all the diseases that nowadays people all around the world suffer, Parkinson's Disease is one of those neuro-degenerative disorders heavily impacting, and unfortu-nately expected to increase, the well-being of, especially, elderly individuals. Besides traditional medical treatments, timely and unobtrusive ways to accurately detect the onset of this disease can rely on Machine Learning (ML) and Deep Learning (DL) techniques, also because of their ability to efficiently extract information from multidimensional data on heterogeneous platforms (including, for instance, constrained Internet of Things devices). This paper presents an experimental performance evaluation of several floating point and quantized ML and DL models which can be deployed efficiently on a tiny microcontroller, namely a STM32U5 micro controller device (available in the STMicroelectronics device cloud). They have been applied to a public Italian voice speech dataset in order to classify the Parkinson Disease in three classes of patients. The experimental results demonstrate the applicability of Neural Network (NN)-based approaches for detecting the disease, as well as the deployability of traditional ML models on tiny resource-constrained devices, allowing a substantial flash memory usage reduction (when compared to non-quantized models) while keeping relatively high accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Among all the diseases that nowadays people all around the world suffer, Parkinson's Disease is one of those neuro-degenerative disorders heavily impacting, and unfortu-nately expected to increase, the well-being of, especially, elderly individuals. Besides traditional medical treatments, timely and unobtrusive ways to accurately detect the onset of this disease can rely on Machine Learning (ML) and Deep Learning (DL) techniques, also because of their ability to efficiently extract information from multidimensional data on heterogeneous platforms (including, for instance, constrained Internet of Things devices). This paper presents an experimental performance evaluation of several floating point and quantized ML and DL models which can be deployed efficiently on a tiny microcontroller, namely a STM32U5 micro controller device (available in the STMicroelectronics device cloud). They have been applied to a public Italian voice speech dataset in order to classify the Parkinson Disease in three classes of patients. The experimental results demonstrate the applicability of Neural Network (NN)-based approaches for detecting the disease, as well as the deployability of traditional ML models on tiny resource-constrained devices, allowing a substantial flash memory usage reduction (when compared to non-quantized models) while keeping relatively high accuracy.
Laura Belli; Luca Davoli; Gianluigi Ferrari
City2i, innovazione IoT per smart city: l’esempio Parma Online
Agenda Digitale 2024, visited: 28.10.2024.
@online{bedafe:2024:agendadigcity2i,
title = {City2i, innovazione IoT per smart city: l’esempio Parma},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},
url = {https://www.agendadigitale.eu/smart-city/city2i-innovazione-iot-per-smart-city-lesempio-parma/},
year = {2024},
date = {2024-10-28},
urldate = {2024-10-28},
organization = {Agenda Digitale},
abstract = {L’integrazione delle tecnologie IoT nelle città intelligenti migliora la qualità della vita urbana. A Parma, la piattaforma city2i® facilita la raccolta e l’analisi dei dati IoT, supportando un’architettura modulare e scalabile. Questo approccio ottimizza le risorse, garantendo sicurezza e interoperabilità per una gestione urbana efficiente.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
L’integrazione delle tecnologie IoT nelle città intelligenti migliora la qualità della vita urbana. A Parma, la piattaforma city2i® facilita la raccolta e l’analisi dei dati IoT, supportando un’architettura modulare e scalabile. Questo approccio ottimizza le risorse, garantendo sicurezza e interoperabilità per una gestione urbana efficiente.
Laura Belli; Luca Davoli; Giulia Oddi; Luca Preite; Martina Galaverni; Tommaso Ganino; Gianluigi Ferrari
IoT-based Data Collection in a Tomato Cultivation Under Different Irrigation Regimes Miscellaneous
2024.
@misc{bedaodprgagafe:2024:mendeleydata,
title = {IoT-based Data Collection in a Tomato Cultivation Under Different Irrigation Regimes},
author = {Laura Belli and Luca Davoli and Giulia Oddi and Luca Preite and Martina Galaverni and Tommaso Ganino and Gianluigi Ferrari},
url = {https://data.mendeley.com/datasets/35wh56287y},
doi = {10.17632/35wh56287y},
year = {2024},
date = {2024-10-24},
urldate = {2024-01-01},
publisher = {Mendeley Data},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Giulia Oddi; Laura Belli; Luca Davoli; Martina Galaverni; Ilaria Marchioni; Margherita Rodolfi; Deborah Beghé; Federico Solari; Giuseppe Vignali; Tommaso Ganino; Gianluigi Ferrari
Optimizing Tomato Production through IoT-based Smart Data Collection and Analysis Inproceedings
In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), pp. 45-50, Bari, Italy, 2024.
@inproceedings{odbedagamarobesovigafe:2024:case,
title = {Optimizing Tomato Production through IoT-based Smart Data Collection and Analysis},
author = {Giulia Oddi and Laura Belli and Luca Davoli and Martina Galaverni and Ilaria Marchioni and Margherita Rodolfi and Deborah Beghé and Federico Solari and Giuseppe Vignali and Tommaso Ganino and Gianluigi Ferrari},
doi = {10.1109/CASE59546.2024.10711738},
year = {2024},
date = {2024-10-23},
urldate = {2024-01-01},
booktitle = {2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
pages = {45-50},
address = {Bari, Italy},
abstract = {The always-growing diffusion and adoption of Internet of Things (IoT) technologies is revolutionizing heterogeneous scenarios (e.g., home, industry, safety, etc.), including the agricultural/farming: this paves the way to the Smart Agriculture (SA) paradigm. In detail, this approach leverages the exploitation of IoT smart objects (e.g., sensors, actuators, ground robots, and flying drones) to optimize and improve agricultural practices, eventually improving both sustainability and efficiency. This paper presents an IoT-based data collection and analysis architecture expedient to acquire and manage IoT data streams generated from the field. The proposed approach has been applied to a real experimental use case to optimize tomato production in the Äzienda Sperimentale Stuard" farm located in Parma, Italy. The obtained results highlight the feasibility, sustainability, and gain margins (i.e., in terms of cost/benefits) returned by such a deployment in a real scenario, enabling farmers to make informed decisions based on on-field data acquisition (e.g., reducing water consumption during the growing season).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The always-growing diffusion and adoption of Internet of Things (IoT) technologies is revolutionizing heterogeneous scenarios (e.g., home, industry, safety, etc.), including the agricultural/farming: this paves the way to the Smart Agriculture (SA) paradigm. In detail, this approach leverages the exploitation of IoT smart objects (e.g., sensors, actuators, ground robots, and flying drones) to optimize and improve agricultural practices, eventually improving both sustainability and efficiency. This paper presents an IoT-based data collection and analysis architecture expedient to acquire and manage IoT data streams generated from the field. The proposed approach has been applied to a real experimental use case to optimize tomato production in the Äzienda Sperimentale Stuard" farm located in Parma, Italy. The obtained results highlight the feasibility, sustainability, and gain margins (i.e., in terms of cost/benefits) returned by such a deployment in a real scenario, enabling farmers to make informed decisions based on on-field data acquisition (e.g., reducing water consumption during the growing season).
Luca Davoli; Hafiz Humza Mahmood Ramzan; Gianluigi Laura Ferrari Belli
CoAP-based Digital Twin Modelling of Heterogeneous IoT Scenarios Inproceedings
In: 10th International Food Operations and Processing Simulation Workshop (FoodOPS 2024), pp. 1-5, Tenerife, Spain, 2024.
@inproceedings{darabefe:2024:foodops,
title = {CoAP-based Digital Twin Modelling of Heterogeneous IoT Scenarios},
author = {Luca Davoli and Hafiz Humza Mahmood Ramzan and Gianluigi Laura Ferrari Belli},
doi = {10.46354/i3m.2024.foodops.016},
year = {2024},
date = {2024-10-20},
urldate = {2024-01-01},
booktitle = {10th International Food Operations and Processing Simulation Workshop (FoodOPS 2024)},
pages = {1-5},
address = {Tenerife, Spain},
abstract = {Modern societies nowadays require more and more abstraction efforts to hide the complexity of underlying systems and infrastructures. To this end, the concept of Digital Twin (DT) has recently emerged as a key enabler for the digital transformation of well-established architectures toward their virtual representation, opening to intelligent processing capabilities (e.g., monitoring, simulation, prediction, optimization). Aside from defining DTs to enhance these services, another key paradigm that is noteworthy of attention is the Internet of Things (IoT), enabling data and information collection through heterogeneous textitsmart devices (often equipped with sensors and actuators). Thus, combining DTs and IoT together with the Constrained Application Protocol (CoAP) as communication protocol (with its native features), will allow to define scalable and lightweight replicas of real systems, and exploit key features (e.g., service and resource discovery) to provide end users with smart solutions. In this paper, a modelling paradigm for heterogeneous IoT scenarios, based on the definition of a DT for each entity involved in a specific context to be mapped, is detailed. This will allow to textita-priori estimate the behaviour of an IoT ecosystem and provide well-known interaction endpoints to request data from/pushing information to the hidden lower layers of the same ecosystem.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern societies nowadays require more and more abstraction efforts to hide the complexity of underlying systems and infrastructures. To this end, the concept of Digital Twin (DT) has recently emerged as a key enabler for the digital transformation of well-established architectures toward their virtual representation, opening to intelligent processing capabilities (e.g., monitoring, simulation, prediction, optimization). Aside from defining DTs to enhance these services, another key paradigm that is noteworthy of attention is the Internet of Things (IoT), enabling data and information collection through heterogeneous textitsmart devices (often equipped with sensors and actuators). Thus, combining DTs and IoT together with the Constrained Application Protocol (CoAP) as communication protocol (with its native features), will allow to define scalable and lightweight replicas of real systems, and exploit key features (e.g., service and resource discovery) to provide end users with smart solutions. In this paper, a modelling paradigm for heterogeneous IoT scenarios, based on the definition of a DT for each entity involved in a specific context to be mapped, is detailed. This will allow to textita-priori estimate the behaviour of an IoT ecosystem and provide well-known interaction endpoints to request data from/pushing information to the hidden lower layers of the same ecosystem.
Sunil Mathew; Eleonora Oliosi; Luca Davoli; Nicolò Strozzi; Andrea Notari; Gianluigi Ferrari
CSI-RSRP-Based Unnecessary Handover Mitigation Through Linear Regression in Dynamic 5G NR Environments Journal Article
In: IEEE Access, 12 , pp. 121808-121821, 2024, ISSN: 2169-3536.
@article{suoldastnofe:2024:access,
title = {CSI-RSRP-Based Unnecessary Handover Mitigation Through Linear Regression in Dynamic 5G NR Environments},
author = {Sunil Mathew and Eleonora Oliosi and Luca Davoli and Nicolò Strozzi and Andrea Notari and Gianluigi Ferrari},
doi = {10.1109/ACCESS.2024.3451483},
issn = {2169-3536},
year = {2024},
date = {2024-08-29},
urldate = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {121808-121821},
abstract = {5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. Because of the presence of an increasing number of available 5G gNodeBs (gNBs), HandOver (HO) management is crucial, especially in terms of Quality of Service (QoS) and Quality of Experience (QoE) perceived by a User Equipment (UE). Unnecessary HandOvers (UHOs) cause latency peaks (on the order hundreds of milliseconds) and multiple throughput drops in 5G communications. In this paper, we first carry out an experimental campaign to investigate the behaviour of latency and throughput in correspondence to UHOs. Then, on the basis of a Matlab-based 5G NR DownLink (DL) transmission simulator, we propose an innovative linear regression-based algorithm to avoid UHOs, which relies on Channel State Information-Reference Signal Received Power (CSI-RSRP) measurements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. Because of the presence of an increasing number of available 5G gNodeBs (gNBs), HandOver (HO) management is crucial, especially in terms of Quality of Service (QoS) and Quality of Experience (QoE) perceived by a User Equipment (UE). Unnecessary HandOvers (UHOs) cause latency peaks (on the order hundreds of milliseconds) and multiple throughput drops in 5G communications. In this paper, we first carry out an experimental campaign to investigate the behaviour of latency and throughput in correspondence to UHOs. Then, on the basis of a Matlab-based 5G NR DownLink (DL) transmission simulator, we propose an innovative linear regression-based algorithm to avoid UHOs, which relies on Channel State Information-Reference Signal Received Power (CSI-RSRP) measurements.
Veronica Mattioli; Luca Davoli; Laura Belli; Sara Gambetta; Luca Carnevali; Andrea Sgoifo; Riccardo Raheli; Gianluigi Ferrari
IoT-Based Assessment of a Driver’s Stress Level Journal Article
In: Sensors, 24 (17), 2024, ISSN: 1424-8220.
@article{madabegacasgrafe:2024:sensors,
title = {IoT-Based Assessment of a Driver’s Stress Level},
author = {Veronica Mattioli and Luca Davoli and Laura Belli and Sara Gambetta and Luca Carnevali and Andrea Sgoifo and Riccardo Raheli and Gianluigi Ferrari},
doi = {10.3390/s24175479},
issn = {1424-8220},
year = {2024},
date = {2024-08-23},
urldate = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {17},
abstract = {Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.
Laura Belli; Luca Davoli; Gianluigi Ferrari; Giulia Oddi
IoT in agricoltura: vantaggi e casi d’uso reali Online
Agenda Digitale 2024, visited: 20.08.2024.
@online{bedafeod:2024:agri,
title = {IoT in agricoltura: vantaggi e casi d’uso reali},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari and Giulia Oddi},
url = {https://www.agendadigitale.eu/mercati-digitali/iot-in-agricoltura-vantaggi-e-casi-duso-reali/},
year = {2024},
date = {2024-08-20},
urldate = {2024-08-20},
organization = {Agenda Digitale},
abstract = {L’integrazione di tecnologie IoT in agricoltura permette un monitoraggio preciso delle coltivazioni, ottimizzando l’uso delle risorse idriche e prevedendo informazioni agronomiche fondamentali. Uno studio dell’Università di Parma esplora i benefici di tali tecnologie, presentando due casi d’uso reali: l’ottimizzazione dell’irrigazione del pomodoro e la predizione del periodo di raccolta del luppolo.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
L’integrazione di tecnologie IoT in agricoltura permette un monitoraggio preciso delle coltivazioni, ottimizzando l’uso delle risorse idriche e prevedendo informazioni agronomiche fondamentali. Uno studio dell’Università di Parma esplora i benefici di tali tecnologie, presentando due casi d’uso reali: l’ottimizzazione dell’irrigazione del pomodoro e la predizione del periodo di raccolta del luppolo.
Anum Nawaz; Laura Belli; Luca Davoli; Gianluigi Ferrari
Hyperledger Fabric in Precision Agriculture: A Study on Data Integrity and Availability Inproceedings
In: 2024 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1-8, Girona, Spain, 2024.
@inproceedings{nabedafe:2024:cits,
title = {Hyperledger Fabric in Precision Agriculture: A Study on Data Integrity and Availability},
author = {Anum Nawaz and Laura Belli and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/CITS61189.2024.10608019},
year = {2024},
date = {2024-07-30},
urldate = {2024-01-01},
booktitle = {2024 International Conference on Computer, Information and Telecommunication Systems (CITS)},
pages = {1-8},
address = {Girona, Spain},
abstract = {The increasing severity of weather events and growing demands for food pose significant challenges to farming and agricultural activities. Over the past decades, the deployment of data acquisition and Internet of Things (IoT)-oriented technologies has emerged as a relevant solution to face these issues, with a primary reason behind this digital agricultural revolution being the cost-effectiveness of data collection in various areas (e.g., soil conditions, crop development, weather patterns). On the basis of these technological advancements, in this paper we discuss on a fully-distributed blockchain-based IoT-oriented agricultural monitoring system based on an integrated Hyperledger Fabric framework. The proposed platform is designed to maximize the efficiency of the approach, to analyze several potential benefits (including, as an example, possible increased food production on reduced land areas, with lower input requirements and a diminished environmental impact), and to effectively aggregate and interpret data into actionable insights for farmers and policymakers. We then propose a preliminary system's deployment, which is instrumental to reflect on platform's scalability, inclusiveness, and modularity. The obtained results highlight its suitability to enhance precision agriculture with secure tracking features.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing severity of weather events and growing demands for food pose significant challenges to farming and agricultural activities. Over the past decades, the deployment of data acquisition and Internet of Things (IoT)-oriented technologies has emerged as a relevant solution to face these issues, with a primary reason behind this digital agricultural revolution being the cost-effectiveness of data collection in various areas (e.g., soil conditions, crop development, weather patterns). On the basis of these technological advancements, in this paper we discuss on a fully-distributed blockchain-based IoT-oriented agricultural monitoring system based on an integrated Hyperledger Fabric framework. The proposed platform is designed to maximize the efficiency of the approach, to analyze several potential benefits (including, as an example, possible increased food production on reduced land areas, with lower input requirements and a diminished environmental impact), and to effectively aggregate and interpret data into actionable insights for farmers and policymakers. We then propose a preliminary system's deployment, which is instrumental to reflect on platform's scalability, inclusiveness, and modularity. The obtained results highlight its suitability to enhance precision agriculture with secure tracking features.
Emanuele Pagliari; Luca Davoli; Gianluigi Ferrari
Wi-Fi-Based Real-Time UAV Localization: A Comparative Analysis Between RSSI-Based and FTM-Based Approaches Journal Article
In: IEEE Transactions on Aerospace and Electronic Systems, 60 (6), pp. 8757-8778, 2024, ISSN: 1557-9603.
@article{padafe:2024:taes,
title = {Wi-Fi-Based Real-Time UAV Localization: A Comparative Analysis Between RSSI-Based and FTM-Based Approaches},
author = {Emanuele Pagliari and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/TAES.2024.3433829},
issn = {1557-9603},
year = {2024},
date = {2024-07-25},
urldate = {2024-01-01},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
volume = {60},
number = {6},
pages = {8757-8778},
abstract = {Wi-Fi connectivity for localization purposes has been used for several years in the Internet of Things (IoT) context, where the (general) static nature of IoT devices allows to approximately localize them in known environments with low effort and implementation costs. While the accuracy of Wi-Fi localization for IoT applications can be considered as acceptable, the adoption of Wi-Fi-based localization for (a highly mobile) unmanned aerial vehicle (UAV) has received limited attention. In this article, a low-cost and low-complexity system architecture is proposed and exploited to perform a comparative analysis between two Wi-Fi-based localization approaches: the traditional received signal strength indicator (RSSI) ranging and the more recent fine time measurement (FTM), based on the IEEE 802.11mc amendment. Our goal is to estimate and compare the efficacy of the proposed system for real-time positioning of a static or moving UAV, evaluating the impact of different filtering solutions on the localization accuracy. The obtained results show that FTM-based localization is more accurate, reducing the positioning error by 37% with respect to the RSSI-based positioning approach. Our results also confirm the better overall performance of the FTM-based solution for low-cost localization applications, discussing its limitations, scalability, and advantages as a viable backup positioning solution in (weak or denied) Global Navigation Satellite System-based environments and scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wi-Fi connectivity for localization purposes has been used for several years in the Internet of Things (IoT) context, where the (general) static nature of IoT devices allows to approximately localize them in known environments with low effort and implementation costs. While the accuracy of Wi-Fi localization for IoT applications can be considered as acceptable, the adoption of Wi-Fi-based localization for (a highly mobile) unmanned aerial vehicle (UAV) has received limited attention. In this article, a low-cost and low-complexity system architecture is proposed and exploited to perform a comparative analysis between two Wi-Fi-based localization approaches: the traditional received signal strength indicator (RSSI) ranging and the more recent fine time measurement (FTM), based on the IEEE 802.11mc amendment. Our goal is to estimate and compare the efficacy of the proposed system for real-time positioning of a static or moving UAV, evaluating the impact of different filtering solutions on the localization accuracy. The obtained results show that FTM-based localization is more accurate, reducing the positioning error by 37% with respect to the RSSI-based positioning approach. Our results also confirm the better overall performance of the FTM-based solution for low-cost localization applications, discussing its limitations, scalability, and advantages as a viable backup positioning solution in (weak or denied) Global Navigation Satellite System-based environments and scenarios.
Armin Mazinani; Danilo Pietro Pau; Luca Davoli; Gianluigi Ferrari
Benchmarking MLCommons Tiny Audio Denoising with Deployability Constraints Inproceedings
In: 2024 IEEE Gaming, Entertainment, and Media Conference (GEM), pp. 1-4, Turin, Italy, 2024, ISSN: 2766-6530.
@inproceedings{mapadafe:2024:gem,
title = {Benchmarking MLCommons Tiny Audio Denoising with Deployability Constraints},
author = {Armin Mazinani and Danilo Pietro Pau and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/GEM61861.2024.10585695},
issn = {2766-6530},
year = {2024},
date = {2024-07-11},
urldate = {2024-01-01},
booktitle = {2024 IEEE Gaming, Entertainment, and Media Conference (GEM)},
pages = {1-4},
address = {Turin, Italy},
abstract = {Speech enhancement is a critical field in audio signal processing given its essentiality to overcome obstacles related to loud and damaged speech signals. Due to the revolutionary capa-bilities of Deep Learning (DL) models, there has been significant interest on benchmarking them and studying their suitability for tiny embedded systems. In this paper, we thoroughly examine the growing field of voice improvement, with a specific emphasis on the use of DL-based techniques under consideration by the MLCommons standardization. In particular, among the others, the Legendre Memory Unit (LMU) model achieves an average Scale-Invariant Signal-to-Distortion Ratio (SISDR) on 8.613 in 627 KiB of FLASH memory, making it deployable on tiny microcontrollers by requiring only 7 ms per inference run.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Speech enhancement is a critical field in audio signal processing given its essentiality to overcome obstacles related to loud and damaged speech signals. Due to the revolutionary capa-bilities of Deep Learning (DL) models, there has been significant interest on benchmarking them and studying their suitability for tiny embedded systems. In this paper, we thoroughly examine the growing field of voice improvement, with a specific emphasis on the use of DL-based techniques under consideration by the MLCommons standardization. In particular, among the others, the Legendre Memory Unit (LMU) model achieves an average Scale-Invariant Signal-to-Distortion Ratio (SISDR) on 8.613 in 627 KiB of FLASH memory, making it deployable on tiny microcontrollers by requiring only 7 ms per inference run.
Martina Galaverni; Ilaria Marchioni; Laura Belli; Tommaso Ganino; Giulia Oddi; Deborah Beghé; Margherita Rodolfi; Luca Davoli; Gianluigi Ferrari
Poster: Evaluation of Hop Cone Maturation through Internet of Things (IoT) and Smart Farming Technologies. A Preliminary Study Inproceedings
In: 39th EBC Congress, pp. 1-1, Lille, France, 2024.
@inproceedings{gamabegaodberodafe:2024:ebc,
title = {Poster: Evaluation of Hop Cone Maturation through Internet of Things (IoT) and Smart Farming Technologies. A Preliminary Study},
author = {Martina Galaverni and Ilaria Marchioni and Laura Belli and Tommaso Ganino and Giulia Oddi and Deborah Beghé and Margherita Rodolfi and Luca Davoli and Gianluigi Ferrari},
year = {2024},
date = {2024-05-30},
urldate = {2024-01-01},
booktitle = {39th EBC Congress},
pages = {1-1},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Laura Belli; Luca Davoli; Gianluigi Ferrari
A Cloud-Oriented Indoor-Outdoor Real-Time Localization IoT Architecture for Industrial Environments Inproceedings
In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), pp. 1-6, Las Vegas, NV, USA, 2024, ISSN: 2331-9860.
@inproceedings{bedafe:2024:iiwot,
title = {A Cloud-Oriented Indoor-Outdoor Real-Time Localization IoT Architecture for Industrial Environments},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/CCNC51664.2024.10454636},
issn = {2331-9860},
year = {2024},
date = {2024-03-18},
urldate = {2024-03-18},
booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},
pages = {1-6},
address = {Las Vegas, NV, USA},
abstract = {Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.
Luca Davoli; Laura Belli; Gianluigi Ferrari; Elisa Londero; Paolo Azzoni
An Edge Computing-Oriented WoT Architecture for Air Quality Monitoring in Mobile Vehicular Scenarios Inproceedings
In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), pp. 1-7, Las Vegas, NV, USA, 2024, ISSN: 2331-9860.
@inproceedings{dabefeloaz:2024:iiwot,
title = {An Edge Computing-Oriented WoT Architecture for Air Quality Monitoring in Mobile Vehicular Scenarios},
author = {Luca Davoli and Laura Belli and Gianluigi Ferrari and Elisa Londero and Paolo Azzoni},
doi = {10.1109/CCNC51664.2024.10454646},
issn = {2331-9860},
year = {2024},
date = {2024-03-18},
urldate = {2024-03-18},
booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},
pages = {1-7},
address = {Las Vegas, NV, USA},
abstract = {Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.
Emanuele Pagliari; Luca Davoli; Giordano Cicioni; Valentina Palazzi; Gianluigi Ferrari
On UAV Terrestrial Connectivity Enhancement through Smart Selective Antennas Journal Article
In: Journal of Physics: Conference Series, 2716 (1), pp. 012057, 2024.
@article{padacipafe:2024:iop,
title = {On UAV Terrestrial Connectivity Enhancement through Smart Selective Antennas},
author = {Emanuele Pagliari and Luca Davoli and Giordano Cicioni and Valentina Palazzi and Gianluigi Ferrari},
doi = {10.1088/1742-6596/2716/1/012057},
year = {2024},
date = {2024-03-17},
urldate = {2024-03-17},
journal = {Journal of Physics: Conference Series},
volume = {2716},
number = {1},
pages = {012057},
publisher = {IOP Publishing},
abstract = {Nowadays, Unmanned Aerial Vehicles (UAVs) are widely used in heterogeneous contexts and, thanks to a continuous technological evolution, are going to be used for several applications such as, for example, Beyond Visual Line of Sight (BVLOS) operations. Since in BVLOS flights the UAV and the ground control center may not have a direct visibility with each other, a robust communication system is needed to provide reliable connectivity. Although a cellular (4G/5G) network is the current best candidate to enable BVLOS applications, there are still some limitations to overcome, as 4G (LTE) and 5G (NR) cellular networks are natively designed for terrestrial use. In this paper, we first investigate current cellular communication limitations for UAV-based applications, in particular taking into account both results available in the literature, as well as experimental performance campaigns. Then, a viable solution for mitigating these drawbacks exploiting selective on-board antennas is proposed, whose performance is experimentally investigated with a preliminary prototypical architecture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nowadays, Unmanned Aerial Vehicles (UAVs) are widely used in heterogeneous contexts and, thanks to a continuous technological evolution, are going to be used for several applications such as, for example, Beyond Visual Line of Sight (BVLOS) operations. Since in BVLOS flights the UAV and the ground control center may not have a direct visibility with each other, a robust communication system is needed to provide reliable connectivity. Although a cellular (4G/5G) network is the current best candidate to enable BVLOS applications, there are still some limitations to overcome, as 4G (LTE) and 5G (NR) cellular networks are natively designed for terrestrial use. In this paper, we first investigate current cellular communication limitations for UAV-based applications, in particular taking into account both results available in the literature, as well as experimental performance campaigns. Then, a viable solution for mitigating these drawbacks exploiting selective on-board antennas is proposed, whose performance is experimentally investigated with a preliminary prototypical architecture.
Soumen Mondal; Luca Davoli; Sanjay Dhar Roy; Sumit Kundu; Gianluigi Ferrari; Riccardo Raheli
Throughput and delay analysis of cognitive M2M communications Journal Article
In: Journal of Network and Computer Applications, 225 , pp. 103856, 2024, ISSN: 1084-8045.
@article{modadhkufera:24:jnca,
title = {Throughput and delay analysis of cognitive M2M communications},
author = {Soumen Mondal and Luca Davoli and Sanjay Dhar Roy and Sumit Kundu and Gianluigi Ferrari and Riccardo Raheli},
doi = {10.1016/j.jnca.2024.103856},
issn = {1084-8045},
year = {2024},
date = {2024-03-04},
urldate = {2024-01-01},
journal = {Journal of Network and Computer Applications},
volume = {225},
pages = {103856},
abstract = {In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.
Sunil Mathew; Eleonora Oliosi; Luca Davoli; Gianluigi Ferrari; Nicolò Strozzi; Andrea Notari
On the Reduction of Unnecessary Handovers Using 5G Small Cells in a 5G NR Environment Inproceedings
In: 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), pp. 1-6, Bangalore, India, 2024.
@inproceedings{maoldafestno:2023:icrvitm,
title = {On the Reduction of Unnecessary Handovers Using 5G Small Cells in a 5G NR Environment},
author = {Sunil Mathew and Eleonora Oliosi and Luca Davoli and Gianluigi Ferrari and Nicolò Strozzi and Andrea Notari},
doi = {10.1109/IC-RVITM60032.2023.10435126},
year = {2024},
date = {2024-02-21},
urldate = {2023-01-01},
booktitle = {2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM)},
pages = {1-6},
address = {Bangalore, India},
abstract = {Nowadays, the deployment of 5G Non-StandAlone (NSA) networks has led to significant enhancements in the User Equipment (UE) experience, in particular in terms of latency reduction and throughput increase. To this end, 5G gNodeBs (gNBs) offer broad coverage but may face challenges in areas with low signal strength, resulting in a Quality of Service (QoS) degradation and too short connections to distant gNBs (denoted as Unnecessary HandOvers, UHOs) due to geographic peculiarities. In order to tackle these issues, in this paper the use of cost-effective 5G small cells in critical areas is considered, aiming at (i) boosting coverage, (ii) enhancing UE QoS, and (iii) avoiding UHOs, thus improving HO performance and UE QoS.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, the deployment of 5G Non-StandAlone (NSA) networks has led to significant enhancements in the User Equipment (UE) experience, in particular in terms of latency reduction and throughput increase. To this end, 5G gNodeBs (gNBs) offer broad coverage but may face challenges in areas with low signal strength, resulting in a Quality of Service (QoS) degradation and too short connections to distant gNBs (denoted as Unnecessary HandOvers, UHOs) due to geographic peculiarities. In order to tackle these issues, in this paper the use of cost-effective 5G small cells in critical areas is considered, aiming at (i) boosting coverage, (ii) enhancing UE QoS, and (iii) avoiding UHOs, thus improving HO performance and UE QoS.
Nikolaos Stathoulopoulos; Emanuele Pagliari; Luca Davoli; George Nikolakopoulos
Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics Inproceedings
In: 2023 21st International Conference on Advanced Robotics (ICAR), pp. 121-127, Abu Dhabi, United Arab Emirates, 2024, ISSN: 2572-6919.
@inproceedings{stpadani:23:icar,
title = {Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics},
author = {Nikolaos Stathoulopoulos and Emanuele Pagliari and Luca Davoli and George Nikolakopoulos},
doi = {10.1109/ICAR58858.2023.10406402},
issn = {2572-6919},
year = {2024},
date = {2024-02-01},
urldate = {2023-12-01},
booktitle = {2023 21st International Conference on Advanced Robotics (ICAR)},
pages = {121-127},
address = {Abu Dhabi, United Arab Emirates},
abstract = {This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.
Antonio Cilfone; Luca Davoli; Gianluigi Ferrari
LoRa Meets IP: A Container-Based Architecture to Virtualize LoRaWAN End Nodes Journal Article
In: IEEE Transactions on Mobile Computing, 23 (10), pp. 9191-9207, 2024, ISSN: 1558-0660.
@article{cidafe:2024:tmc,
title = {LoRa Meets IP: A Container-Based Architecture to Virtualize LoRaWAN End Nodes},
author = {Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/TMC.2024.3359150},
issn = {1558-0660},
year = {2024},
date = {2024-01-26},
urldate = {2024-10-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {23},
number = {10},
pages = {9191-9207},
abstract = {In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes’ side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes’ side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.
Luca Davoli; Laura Belli; Alessandro Dall'Olio; Francesco Di Nocera; Paolo Adorni; Alessandro Cantelli; Gianluigi Ferrari
Data Integration in a Smart City: A Real Case Book Chapter
In: Information and Communications Technologies for Smart Cities and Societies, 5 , Chapter 2, pp. 11-24, Springer Nature Switzerland, 2024.
@inbook{dabedadiadcafe:2023:thecityproject,
title = {Data Integration in a Smart City: A Real Case},
author = {Luca Davoli and Laura Belli and Alessandro Dall'Olio and Francesco Di Nocera and Paolo Adorni and Alessandro Cantelli and Gianluigi Ferrari},
doi = {10.1007/978-3-031-39446-1_2},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Information and Communications Technologies for Smart Cities and Societies},
volume = {5},
pages = {11-24},
publisher = {Springer Nature Switzerland},
chapter = {2},
series = {THE CITY PROJECT},
abstract = {The introduction and continuous integration of Internet of Things (IoT)-oriented technologies in urban environments leads to enhanced solutions in several domains (such as mobility, health, energy management, environmental monitoring, etc.), thus making a city “smart” and ultimately benefiting the everyday life of its citizens. As IoT systems are widely known to be producers of (often a very large amount of) heterogeneous data, in this chapter we discuss a modular and scalable approach to handle IoT-based data collection and management in a real smart city case, namely, that of the city of Parma, Italy. The proposed IoT infrastructure, the core component of which is a logical processing entity, acting as middleware and denoted as “city2i®,” in charge of "digesting" the heterogeneous information generated by multiple data sources, allows the municipality to monitor the city status (from multiple perspectives) and to highlight “hidden” correlations among collected data.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The introduction and continuous integration of Internet of Things (IoT)-oriented technologies in urban environments leads to enhanced solutions in several domains (such as mobility, health, energy management, environmental monitoring, etc.), thus making a city “smart” and ultimately benefiting the everyday life of its citizens. As IoT systems are widely known to be producers of (often a very large amount of) heterogeneous data, in this chapter we discuss a modular and scalable approach to handle IoT-based data collection and management in a real smart city case, namely, that of the city of Parma, Italy. The proposed IoT infrastructure, the core component of which is a logical processing entity, acting as middleware and denoted as “city2i®,” in charge of "digesting" the heterogeneous information generated by multiple data sources, allows the municipality to monitor the city status (from multiple perspectives) and to highlight “hidden” correlations among collected data.
2023
Emanuele Pagliari; Luca Davoli; Giordano Cicioni; Valentina Palazzi; Paolo Mezzanotte; Federico Alimenti; Luca Roselli; Gianluigi Ferrari
Smart Selective Antennas System (SSAS): Improving 4G LTE Connectivity for UAVs Using Directive Selective Antennas Journal Article
In: IEEE Access, 12 , pp. 7040-7062, 2023, ISSN: 2169-3536.
@article{padacipamealrofe:2023:access,
title = {Smart Selective Antennas System (SSAS): Improving 4G LTE Connectivity for UAVs Using Directive Selective Antennas},
author = {Emanuele Pagliari and Luca Davoli and Giordano Cicioni and Valentina Palazzi and Paolo Mezzanotte and Federico Alimenti and Luca Roselli and Gianluigi Ferrari},
doi = {10.1109/ACCESS.2023.3347335},
issn = {2169-3536},
year = {2023},
date = {2023-12-25},
urldate = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {7040-7062},
abstract = {In this paper, the prototypical deployment of a Multiple-Input-Multiple-Output (MIMO) antennas system, denoted as Smart Selective Antennas System (SSAS), aiming at mitigating inter-cell interference effects of cellular networks for in-flight Unmanned Aerial Vehicles (UAVs), is discussed. In detail, the proposed SSAS is beneficial to increase the communication reliability over existing cellular networks, especially with regard to complex Beyond Visual Line of Sight (BVLOS) drones’ missions and applications. Its deployment is motivated as existing 4G Long Term Evolution (LTE) cellular networks (as well as 5G networks) are mainly designed and optimized for terrestrial utilization, thus not taking into account interference effects on flying connected devices. The prototypical implementation of the SSAS has been expedient to conduct multiple experimental flights with a drone at different altitudes, collecting performance results and validating the proposed SSAS as a viable solution for inter-cell interference mitigation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, the prototypical deployment of a Multiple-Input-Multiple-Output (MIMO) antennas system, denoted as Smart Selective Antennas System (SSAS), aiming at mitigating inter-cell interference effects of cellular networks for in-flight Unmanned Aerial Vehicles (UAVs), is discussed. In detail, the proposed SSAS is beneficial to increase the communication reliability over existing cellular networks, especially with regard to complex Beyond Visual Line of Sight (BVLOS) drones’ missions and applications. Its deployment is motivated as existing 4G Long Term Evolution (LTE) cellular networks (as well as 5G networks) are mainly designed and optimized for terrestrial utilization, thus not taking into account interference effects on flying connected devices. The prototypical implementation of the SSAS has been expedient to conduct multiple experimental flights with a drone at different altitudes, collecting performance results and validating the proposed SSAS as a viable solution for inter-cell interference mitigation.
Armin Mazinani; Luca Davoli; Danilo Pietro Pau; Gianluigi Ferrari
Accurate Classification of Sport Activities Under Tiny Deployability Constraints Inproceedings
In: 2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), pp. 261-267, Bali, Indonesia, 2023, ISSN: 2832-1383.
@inproceedings{madapafe:2023:iotais,
title = {Accurate Classification of Sport Activities Under Tiny Deployability Constraints},
author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},
doi = {10.1109/IoTaIS60147.2023.10346056},
issn = {2832-1383},
year = {2023},
date = {2023-12-14},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)},
pages = {261-267},
address = {Bali, Indonesia},
abstract = {Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.
Luca Davoli; Laura Belli; Gianluigi Ferrari
Air quality dataset from an indoor airport travelers transit area Journal Article
In: Data in Brief, 52 , pp. 109821, 2023, ISSN: 2352-3409.
@article{dabefe:2023:dib,
title = {Air quality dataset from an indoor airport travelers transit area},
author = {Luca Davoli and Laura Belli and Gianluigi Ferrari},
doi = {10.1016/j.dib.2023.109821},
issn = {2352-3409},
year = {2023},
date = {2023-11-20},
urldate = {2024-01-01},
journal = {Data in Brief},
volume = {52},
pages = {109821},
abstract = {The experimental dataset (organized in a semicolon-separated text format) is composed by air quality records collected over a 1-year period (October 2022-October 2023) in an indoor travelers’ transit area in the Brindisi airport, Italy. In detail, the dataset consists of three CSV files (ranging from 7M records to 11M records) resulting from the on-field data collection performed by three prototypical Internet of Things (IoT) sensing nodes, designed and implemented at the IoTLab of the University of Parma, Italy, featuring a Raspberry Pi 4 (as processing unit) which three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30) are connected to. The sensors sample the air in the monitored static indoor environment every 2 s. Each collected record composing the experimental dataset contains (i) the identifier of the IoT node that sampled the air parameters; (ii) the presence of gases (as a unified value concentration); (iii) the concentration of carbon dioxide (CO2) in the travelers’ transit area, together with air temperature and humidity; and (iv) the concentration of particulate matter (PM) in the indoor monitored environment – in terms of particles’ mass concentration (µg/m3), number of particles (#/cm3), and typical particle size (µm) – for particles with a diameter up to 0.5 µm (PM0.5), 1 µm (PM1), 2.5 µm (PM2.5), 4 µm (PM4), and 10 µm (PM10). Therefore, on the basis of the monitored air parameters in the indoor travelers’ transit area, the experimental dataset might be expedient for further analyses – e.g., for calculating Air Quality Indexes (AQIs) taking into account the collected information – and for comparison with information sampled in different contexts and scenarios – examples could be indoor domestic environments, as well as outdoor monitoring in smart cities or public transports.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The experimental dataset (organized in a semicolon-separated text format) is composed by air quality records collected over a 1-year period (October 2022-October 2023) in an indoor travelers’ transit area in the Brindisi airport, Italy. In detail, the dataset consists of three CSV files (ranging from 7M records to 11M records) resulting from the on-field data collection performed by three prototypical Internet of Things (IoT) sensing nodes, designed and implemented at the IoTLab of the University of Parma, Italy, featuring a Raspberry Pi 4 (as processing unit) which three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30) are connected to. The sensors sample the air in the monitored static indoor environment every 2 s. Each collected record composing the experimental dataset contains (i) the identifier of the IoT node that sampled the air parameters; (ii) the presence of gases (as a unified value concentration); (iii) the concentration of carbon dioxide (CO2) in the travelers’ transit area, together with air temperature and humidity; and (iv) the concentration of particulate matter (PM) in the indoor monitored environment – in terms of particles’ mass concentration (µg/m3), number of particles (#/cm3), and typical particle size (µm) – for particles with a diameter up to 0.5 µm (PM0.5), 1 µm (PM1), 2.5 µm (PM2.5), 4 µm (PM4), and 10 µm (PM10). Therefore, on the basis of the monitored air parameters in the indoor travelers’ transit area, the experimental dataset might be expedient for further analyses – e.g., for calculating Air Quality Indexes (AQIs) taking into account the collected information – and for comparison with information sampled in different contexts and scenarios – examples could be indoor domestic environments, as well as outdoor monitoring in smart cities or public transports.
Armin Mazinani; Luca Davoli; Gianluigi Ferrari
Deep Learning-Based Cryptocurrency Price Prediction: A Comparative Analysis Inproceedings
In: 2023 5th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), pp. 1-8, Paris, France, 2023.
@inproceedings{madafe:2023:brains,
title = {Deep Learning-Based Cryptocurrency Price Prediction: A Comparative Analysis},
author = {Armin Mazinani and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/BRAINS59668.2023.10317011},
year = {2023},
date = {2023-11-17},
urldate = {2023-01-01},
booktitle = {2023 5th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS)},
pages = {1-8},
address = {Paris, France},
abstract = {In recent years, cryptocurrencies have gained a lot of popularity in the financial markets and now, in addition to investing on them, it is possible to use them as a common currency to meet daily needs. Given the complex nature of financial markets and their reliance on different parameters to determine stocks' and assets' prices, the ability to predict prices is important for investment decisions, especially with respect to cryptocurrencies. To this end, Deep Learning (DL)-based algorithms can be viable solutions, owing to their use as time series forecasting tools. In this paper, we investigate the applicability of DL algorithms to forecast the prices of three cryptocurrencies, namely Bitcoin, Ethereum, and Ripple. We evaluate the performance of the proposed approach, in terms of short-term and long-term prediction accuracy (considering proper error metrics).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, cryptocurrencies have gained a lot of popularity in the financial markets and now, in addition to investing on them, it is possible to use them as a common currency to meet daily needs. Given the complex nature of financial markets and their reliance on different parameters to determine stocks' and assets' prices, the ability to predict prices is important for investment decisions, especially with respect to cryptocurrencies. To this end, Deep Learning (DL)-based algorithms can be viable solutions, owing to their use as time series forecasting tools. In this paper, we investigate the applicability of DL algorithms to forecast the prices of three cryptocurrencies, namely Bitcoin, Ethereum, and Ripple. We evaluate the performance of the proposed approach, in terms of short-term and long-term prediction accuracy (considering proper error metrics).
Luca Davoli; Laura Belli; Gianluigi Ferrari
Indoor Air Quality Monitoring @ Brindisi Airport Miscellaneous
2023.
@misc{dabefe:2023:mendeleydata,
title = {Indoor Air Quality Monitoring @ Brindisi Airport},
author = {Luca Davoli and Laura Belli and Gianluigi Ferrari},
url = {https://data.mendeley.com/datasets/bv2hvm4pmz},
doi = {10.17632/BV2HVM4PMZ},
year = {2023},
date = {2023-11-10},
urldate = {2023-01-01},
publisher = {Mendeley Data},
abstract = {The experimental dataset here represented is composed by 3 CSV files (ranging from 7M records to 11M records), each corresponding to air quality records -- related to the presence of gases (as a unified value concentration); the concentration of carbon dioxide (CO2), together with air temperature and humidity; and the concentration of particulate matter (PM) in the monitored environment (PM0.5, PM1, PM2.5, PM4, PM10) -- sampled (every 2 sec) over a 1-year period (October 2022-October 2023) in an indoor travelers’ transit area in the Brindisi airport, Italy, in the aim of the European project InSecTT (https://www.insectt.eu/, https://cordis.europa.eu/project/id/876038/).
In particular, each CSV file has been generated by a prototypical Internet of Things (IoT) sensing node, designed at the IoTLab (https://iotlab.unipr.it/) of the University of Parma, Italy, exploiting a Raspberry Pi 4 (as processing unit) and three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30). Then, as a time reference, each record contains the Unix-like data collection timestamp and the identity of the IoT node sampling the air parameters (for safety purposes, the association with a generic color name in the CSV file name has been a consequence of an anonymization naming process for the IoT nodes, in order to hide their precise positions inside the airside transit area).},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The experimental dataset here represented is composed by 3 CSV files (ranging from 7M records to 11M records), each corresponding to air quality records -- related to the presence of gases (as a unified value concentration); the concentration of carbon dioxide (CO2), together with air temperature and humidity; and the concentration of particulate matter (PM) in the monitored environment (PM0.5, PM1, PM2.5, PM4, PM10) -- sampled (every 2 sec) over a 1-year period (October 2022-October 2023) in an indoor travelers’ transit area in the Brindisi airport, Italy, in the aim of the European project InSecTT (https://www.insectt.eu/, https://cordis.europa.eu/project/id/876038/).
In particular, each CSV file has been generated by a prototypical Internet of Things (IoT) sensing node, designed at the IoTLab (https://iotlab.unipr.it/) of the University of Parma, Italy, exploiting a Raspberry Pi 4 (as processing unit) and three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30). Then, as a time reference, each record contains the Unix-like data collection timestamp and the identity of the IoT node sampling the air parameters (for safety purposes, the association with a generic color name in the CSV file name has been a consequence of an anonymization naming process for the IoT nodes, in order to hide their precise positions inside the airside transit area).
In particular, each CSV file has been generated by a prototypical Internet of Things (IoT) sensing node, designed at the IoTLab (https://iotlab.unipr.it/) of the University of Parma, Italy, exploiting a Raspberry Pi 4 (as processing unit) and three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30). Then, as a time reference, each record contains the Unix-like data collection timestamp and the identity of the IoT node sampling the air parameters (for safety purposes, the association with a generic color name in the CSV file name has been a consequence of an anonymization naming process for the IoT nodes, in order to hide their precise positions inside the airside transit area).
Andrea Abrardo; Patrizia Agnello; Silvia M. Ansaldi; Laura Belli; Paolo Bragatto; Luca Davoli; Francesca M. Fabiani; Gianluigi Ferrari; Lorenzo Parri
CP-SEC: Sistema Cyber-Fisico per la sicurezza dei lavoratori in presenza di sostanze pericolose Technical Report
(22), 2023, ISBN: 9788874848072.
@techreport{abaganbebrdafafepa:2023:inail,
title = {CP-SEC: Sistema Cyber-Fisico per la sicurezza dei lavoratori in presenza di sostanze pericolose},
author = {Andrea Abrardo and Patrizia Agnello and Silvia M. Ansaldi and Laura Belli and Paolo Bragatto and Luca Davoli and Francesca M. Fabiani and Gianluigi Ferrari and Lorenzo Parri},
url = {https://www.inail.it/cs/internet/comunicazione/pubblicazioni/quaderni-di-ricerca/quad-ric-numero-22-maggio-2023.html},
isbn = {9788874848072},
year = {2023},
date = {2023-10-04},
urldate = {2023-10-04},
number = {22},
publisher = {Inail Quaderni di ricerca},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Eleonora Oliosi; Sunil Mathew; Luca Davoli; Gianluigi Ferrari; Nicolo Strozzi; Filippo Manghi; Fabio Casoli; Andrea Notari
Simulation-based Analysis of Experimental 5G NR Downlink CSI-RSRP-based Handover Performance Inproceedings
In: 28th European Wireless Conference (European Wireless 2023), pp. 8-13, Rome, Italy, 2023.
@inproceedings{olmadafestmacano:2023:ew,
title = {Simulation-based Analysis of Experimental 5G NR Downlink CSI-RSRP-based Handover Performance},
author = {Eleonora Oliosi and Sunil Mathew and Luca Davoli and Gianluigi Ferrari and Nicolo Strozzi and Filippo Manghi and Fabio Casoli and Andrea Notari},
url = {https://ieeexplore.ieee.org/document/10477096},
year = {2023},
date = {2023-10-04},
urldate = {2024-03-20},
booktitle = {28th European Wireless Conference (European Wireless 2023)},
pages = {8-13},
address = {Rome, Italy},
abstract = {In the last years, 5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. The increasing number of available 5G gNodeBs (gNBs) makes handover management crucial, especially from the point of view of the Quality of Experience (QoE) of a User Equipment (UE). In order to analyse the handover behaviour in 5G networks, we derive a Matlab-based simulator for location-based handover management. As a first step, an accurate simulation model of an experimental 5G NR communication system is derived. In detail, the performance results are expressed in terms of Reference Signal Received Power (RSRP) 5G NR signal quality metric, measured on the Channel State Information (CSI) Reference Signal (RS) on a DownLink (DL) transmission from a gNB to a mobile UE. In order to derive an accurate simulator, we rely on an experimental testbed based on the use of a commercial wireless 5G embedded modem positioned in a moving vehicle and collecting geolocalized RSRP values.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last years, 5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. The increasing number of available 5G gNodeBs (gNBs) makes handover management crucial, especially from the point of view of the Quality of Experience (QoE) of a User Equipment (UE). In order to analyse the handover behaviour in 5G networks, we derive a Matlab-based simulator for location-based handover management. As a first step, an accurate simulation model of an experimental 5G NR communication system is derived. In detail, the performance results are expressed in terms of Reference Signal Received Power (RSRP) 5G NR signal quality metric, measured on the Channel State Information (CSI) Reference Signal (RS) on a DownLink (DL) transmission from a gNB to a mobile UE. In order to derive an accurate simulator, we rely on an experimental testbed based on the use of a commercial wireless 5G embedded modem positioned in a moving vehicle and collecting geolocalized RSRP values.
Emanuele Pagliari; Luca Davoli; Gianluigi Ferrari
Harnessing Communication Heterogeneity: Architectural Design, Analytical Modeling, and Performance Evaluation of an IoT Multi-Interface Gateway Journal Article
In: IEEE Internet of Things Journal, 11 (5), pp. 8030-8051, 2023.
@article{padafe:2023:iotj,
title = {Harnessing Communication Heterogeneity: Architectural Design, Analytical Modeling, and Performance Evaluation of an IoT Multi-Interface Gateway},
author = {Emanuele Pagliari and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/JIOT.2023.3317672},
year = {2023},
date = {2023-09-20},
urldate = {2024-01-01},
journal = {IEEE Internet of Things Journal},
volume = {11},
number = {5},
pages = {8030-8051},
abstract = {Given the massive deployment of Internet of Things (IoT) applications over the last decade, the need for gateways able to efficiently route information flows across multiple heterogeneous networks has emerged, bringing new challenges. Therefore, the design and implementation of IoT gateways is crucial. In this article, with reference to the architecture of a prototypical multi-interface gateway (MIG) (based on commercial-off-the-shelf (COTS) devices), we evaluate its performance: 1) analytically, through an innovative Markov chain-based model; 2) by simulation, with a Python simulator; and 3) experimentally, through the (starting) COTS device-based prototype. In detail, the MIG is equipped with heterogeneous wireless communication interfaces (namely, LoRaWAN, BLE, cellular 4G Cat. 4, and IEEE 802.11 Wi-Fi 2.4 GHz) and is applicable to multiple IoT scenarios. The obtained simulation and experimental results show the validity of the proposed analytical model. Further improvements of the proposed framework are eventually discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Given the massive deployment of Internet of Things (IoT) applications over the last decade, the need for gateways able to efficiently route information flows across multiple heterogeneous networks has emerged, bringing new challenges. Therefore, the design and implementation of IoT gateways is crucial. In this article, with reference to the architecture of a prototypical multi-interface gateway (MIG) (based on commercial-off-the-shelf (COTS) devices), we evaluate its performance: 1) analytically, through an innovative Markov chain-based model; 2) by simulation, with a Python simulator; and 3) experimentally, through the (starting) COTS device-based prototype. In detail, the MIG is equipped with heterogeneous wireless communication interfaces (namely, LoRaWAN, BLE, cellular 4G Cat. 4, and IEEE 802.11 Wi-Fi 2.4 GHz) and is applicable to multiple IoT scenarios. The obtained simulation and experimental results show the validity of the proposed analytical model. Further improvements of the proposed framework are eventually discussed.
Armin Mazinani; Luca Davoli; Danilo Pietro Pau; Gianluigi Ferrari
Air Quality Estimation with Embedded AI-Based Prediction Algorithms Inproceedings
In: 2023 International Conference on Information Technology Research and Innovation (ICITRI), pp. 87-92, Jakarta, Indonesia, 2023.
@inproceedings{madapafe:2023:icitri,
title = {Air Quality Estimation with Embedded AI-Based Prediction Algorithms},
author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},
doi = {10.1109/ICITRI59340.2023.10249864},
year = {2023},
date = {2023-09-19},
urldate = {2023-09-19},
booktitle = {2023 International Conference on Information Technology Research and Innovation (ICITRI)},
pages = {87-92},
address = {Jakarta, Indonesia},
abstract = {Air pollution is one of the main criticalities in cities with large populations. Therefore, accurate air quality prediction is crucial to control the environmental pollution and to maintain healthy living conditions for the citizens. To this end, particulate matters (e.g., PM2.5) have been recognised as one of the most important pollutants with a detrimental impact on human health. In this paper, we investigate the trade-off between estimation accuracy and computational complexity of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting air pollution (in terms of PM2.5 concentration), in order to investigate their applicability to Internet of Things (IoT)-oriented applications. Six DL methods are implemented and evaluated, considering various time lags. DL approaches are shown to outperform ML approaches—in the DL case, two distinct optimizers, namely ADAM and Root Mean Squared Propagation (RMSProp), are considered. Among all algorithms evaluated, GRU had a RMSE of 20.02, while SimpleRNN reduced the MACs number by 98.90% over GRU and with an accuracy drop of 7.5%.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Air pollution is one of the main criticalities in cities with large populations. Therefore, accurate air quality prediction is crucial to control the environmental pollution and to maintain healthy living conditions for the citizens. To this end, particulate matters (e.g., PM2.5) have been recognised as one of the most important pollutants with a detrimental impact on human health. In this paper, we investigate the trade-off between estimation accuracy and computational complexity of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting air pollution (in terms of PM2.5 concentration), in order to investigate their applicability to Internet of Things (IoT)-oriented applications. Six DL methods are implemented and evaluated, considering various time lags. DL approaches are shown to outperform ML approaches—in the DL case, two distinct optimizers, namely ADAM and Root Mean Squared Propagation (RMSProp), are considered. Among all algorithms evaluated, GRU had a RMSE of 20.02, while SimpleRNN reduced the MACs number by 98.90% over GRU and with an accuracy drop of 7.5%.
Roberta Stefanini; Luca Preite; Eleonora Bottani; Laura Belli; Luca Davoli; Gianluigi Ferrari; Giuseppe Vignali
Selection of 4.0 sensors for small holders: the compromise between the advantages and the costs of the implementation Inproceedings
In: Proceedings of the 9th International Food Operations and Processing Simulation Workshop (FoodOPS 2023), pp. 1-7, Athens, Greece, 2023.
@inproceedings{stprbobedafevi:2023:foodops,
title = {Selection of 4.0 sensors for small holders: the compromise between the advantages and the costs of the implementation},
author = {Roberta Stefanini and Luca Preite and Eleonora Bottani and Laura Belli and Luca Davoli and Gianluigi Ferrari and Giuseppe Vignali},
doi = {10.46354/i3m.2023.foodops.007},
year = {2023},
date = {2023-09-19},
urldate = {2023-01-01},
booktitle = {Proceedings of the 9th International Food Operations and Processing Simulation Workshop (FoodOPS 2023)},
pages = {1-7},
address = {Athens, Greece},
abstract = {The agricultural sector involves various environmental impacts, related to soil exploitation, water consumption and greenhouse gasses emissions. The advent of 4.0 technologies could help reduce them, e.g., by using sensors that constantly control the field. However, these solutions are often implemented by big producers that can easily bear their costs. Thence, in the case of small holders, can the benefits achievable with 4.0 technologies justify their implementation costs? To answer this question, an Italian field with three rows of tomatoes has been investigated as a case study. A row with a traditional irrigation system has been compared to two rows with a 60% irrigation scenario monitored with 4.0 sensors. Overall, one environmental sensor, three crop analysis sensors, three flowmeters, three valves and one network infrastructure have been selected and introduced. The key findings of the work allow for quantifying the amount of water that small holders can save; the positive Net Present Value recommends the investment, with a Pay Back Period of 1.9 years. In the next steps, additional 4.0 sensors will be tested in the agricultural supply chain of some selected small holders in the Mediterranean area, to check whether the 4.0 implementation could not only reduce water consumption, but also improve storage conditions and reduce wastage.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The agricultural sector involves various environmental impacts, related to soil exploitation, water consumption and greenhouse gasses emissions. The advent of 4.0 technologies could help reduce them, e.g., by using sensors that constantly control the field. However, these solutions are often implemented by big producers that can easily bear their costs. Thence, in the case of small holders, can the benefits achievable with 4.0 technologies justify their implementation costs? To answer this question, an Italian field with three rows of tomatoes has been investigated as a case study. A row with a traditional irrigation system has been compared to two rows with a 60% irrigation scenario monitored with 4.0 sensors. Overall, one environmental sensor, three crop analysis sensors, three flowmeters, three valves and one network infrastructure have been selected and introduced. The key findings of the work allow for quantifying the amount of water that small holders can save; the positive Net Present Value recommends the investment, with a Pay Back Period of 1.9 years. In the next steps, additional 4.0 sensors will be tested in the agricultural supply chain of some selected small holders in the Mediterranean area, to check whether the 4.0 implementation could not only reduce water consumption, but also improve storage conditions and reduce wastage.
Emanuele Pagliari; Luca Davoli; Giordano Cicioni; Valentina Palazzi; Gianluigi Ferrari
On UAV Terrestrial Connectivity Enhancement through Smart Selective Antennas Unpublished
2023, (13th EASN International Conference).
@unpublished{padacipafe:2023:easn,
title = {On UAV Terrestrial Connectivity Enhancement through Smart Selective Antennas},
author = {Emanuele Pagliari and Luca Davoli and Giordano Cicioni and Valentina Palazzi and Gianluigi Ferrari},
url = {https://easnconference.eu/2023/home},
year = {2023},
date = {2023-09-08},
urldate = {2023-01-01},
note = {13th EASN International Conference},
keywords = {},
pubstate = {published},
tppubtype = {unpublished}
}
Veronica Mattioli; Luca Davoli; Laura Belli; Gianluigi Ferrari; Riccardo Raheli
Thermal Camera-based Driver Monitoring in the Automotive Scenario Inproceedings
In: 2023 AEIT International Conference on Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), pp. 1-6, Modena, Italy, 2023.
@inproceedings{madabefera:2023:aeit,
title = {Thermal Camera-based Driver Monitoring in the Automotive Scenario},
author = {Veronica Mattioli and Luca Davoli and Laura Belli and Gianluigi Ferrari and Riccardo Raheli},
doi = {10.23919/AEITAUTOMOTIVE58986.2023.10217235},
year = {2023},
date = {2023-08-23},
urldate = {2023-08-23},
booktitle = {2023 AEIT International Conference on Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)},
pages = {1-6},
address = {Modena, Italy},
abstract = {Driver monitoring in the automotive scenario is a key task in preventing hazardous events, such as road accidents. To this purpose, driver monitoring systems aim at providing prompt assistance in case of anomalies, such as alterations of the driver’s psycho-physiological status. The driving performance may, indeed, be negatively affected by high levels of stress and workload, that might be caused by different factors.In this paper, a novel in-vehicle monitoring system to extract the skin temperature of a driver, as one of the main indicators of his/her psycho-physiological status, is proposed. Thermal imaging techniques and video processing algorithms are jointly employed to record and extract temperature-related information in a non-invasive and contactless way. Temperature variations detected on the subject’s face are regulated by the activity of the Autonomic Nervous System (ANS) and represent, indeed, an important index of perceived stress levels. The feasibility of the proposed method is assessed both in simulated and real driving scenarios on the basis of experimental results.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Driver monitoring in the automotive scenario is a key task in preventing hazardous events, such as road accidents. To this purpose, driver monitoring systems aim at providing prompt assistance in case of anomalies, such as alterations of the driver’s psycho-physiological status. The driving performance may, indeed, be negatively affected by high levels of stress and workload, that might be caused by different factors.In this paper, a novel in-vehicle monitoring system to extract the skin temperature of a driver, as one of the main indicators of his/her psycho-physiological status, is proposed. Thermal imaging techniques and video processing algorithms are jointly employed to record and extract temperature-related information in a non-invasive and contactless way. Temperature variations detected on the subject’s face are regulated by the activity of the Autonomic Nervous System (ANS) and represent, indeed, an important index of perceived stress levels. The feasibility of the proposed method is assessed both in simulated and real driving scenarios on the basis of experimental results.
Laura Belli; Luca Davoli; Gianluigi Ferrari
Smart City as an Urban Intelligent Digital System: The Case of Parma Journal Article
In: Computer, 56 (7), pp. 106-109, 2023, ISSN: 1558-0814.
@article{bedafe:2023:computer,
title = {Smart City as an Urban Intelligent Digital System: The Case of Parma},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},
doi = {10.1109/MC.2023.3267245},
issn = {1558-0814},
year = {2023},
date = {2023-06-16},
urldate = {2023-07-01},
journal = {Computer},
volume = {56},
number = {7},
pages = {106-109},
abstract = {A smart city is an intelligent digital system that implements an effective smart urban environment able to integrate information from heterogeneous data sources and to provide efficient high-level services to citizens and municipal authorities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A smart city is an intelligent digital system that implements an effective smart urban environment able to integrate information from heterogeneous data sources and to provide efficient high-level services to citizens and municipal authorities.
Fabrizio Carpi; Marco Martalò; Luca Davoli; Antonio Cilfone; Yingjie Yu; Yi Wang; Gianluigi Ferrari
Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications Journal Article
In: Computer Networks, 225 , pp. 109663, 2023, ISSN: 1389-1286.
@article{camadaciyuwafe:2023:comnet,
title = {Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications},
author = {Fabrizio Carpi and Marco Martalò and Luca Davoli and Antonio Cilfone and Yingjie Yu and Yi Wang and Gianluigi Ferrari},
doi = {10.1016/j.comnet.2023.109663},
issn = {1389-1286},
year = {2023},
date = {2023-03-06},
urldate = {2023-01-01},
journal = {Computer Networks},
volume = {225},
pages = {109663},
abstract = {In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).
Christian Hirsch; Luca Davoli; Radu Grosu; Gianluigi Ferrari
DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks Journal Article
In: Computer Networks, 222 , pp. 109560, 2023, ISSN: 1389-1286.
@article{hidagrfe:2023,
title = {DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks},
author = {Christian Hirsch and Luca Davoli and Radu Grosu and Gianluigi Ferrari},
doi = {10.1016/j.comnet.2023.109560},
issn = {1389-1286},
year = {2023},
date = {2023-01-10},
urldate = {2023-01-01},
journal = {Computer Networks},
volume = {222},
pages = {109560},
abstract = {Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37µA per data transmission and around 41.37µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37µA per data transmission and around 41.37µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.
2022
Luca Davoli; Laura Belli; Francesco Denaro; Dinesh Tamang; Andrea Abrardo; Gianluigi Ferrari
On Safety Enhancement in IIoT Scenarios Through Heterogeneous Localization Techniques Journal Article
In: Chemical Engineering Transactions, 91 , pp. 259-264, 2022.
@article{dabedetaabfe:2022:cisap10,
title = {On Safety Enhancement in IIoT Scenarios Through Heterogeneous Localization Techniques},
author = {Luca Davoli and Laura Belli and Francesco Denaro and Dinesh Tamang and Andrea Abrardo and Gianluigi Ferrari},
doi = {10.3303/CET2291044},
year = {2022},
date = {2022-06-15},
urldate = {2022-06-01},
journal = {Chemical Engineering Transactions},
volume = {91},
pages = {259-264},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Luca Davoli; Gianluigi Ferrari
Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications Book
Institution of Engineering and Technology, 2022.
@book{dafe:2022:iet,
title = {Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications},
author = {Luca Davoli and Gianluigi Ferrari},
editor = {Luca Davoli and Gianluigi Ferrari},
doi = {10.1049/PBTE101E},
year = {2022},
date = {2022-06-15},
urldate = {2022-06-15},
publisher = {Institution of Engineering and Technology},
series = {Telecommunications},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Luca Davoli; Massimo Moreni; Gianluigi Ferrari
A Sink-oriented Routing Protocol for Blue Light Link-based Mesh Network Book Chapter
In: Davoli, Luca; Ferrari, Gianluigi (Ed.): Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications, pp. 21-31, Institution of Engineering and Technology, 2022.
@inbook{damofe:2022:bll,
title = {A Sink-oriented Routing Protocol for Blue Light Link-based Mesh Network},
author = { Luca Davoli and Massimo Moreni and Gianluigi Ferrari},
editor = {Luca Davoli and Gianluigi Ferrari},
doi = {10.1049/PBTE101E_ch2},
year = {2022},
date = {2022-06-15},
urldate = {2022-01-01},
booktitle = {Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications},
pages = {21-31},
publisher = {Institution of Engineering and Technology},
series = {Telecommunications},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Antonio Cilfone; Luca Davoli; Laura Belli; Gianluigi Ferrari
Seamless IoT Mobile Sensing through Wi-Fi Mesh Networking Book Chapter
In: Davoli, Luca; Ferrari, Gianluigi (Ed.): Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications, pp. 67-80, Institution of Engineering and Technology, 2022.
@inbook{cidabefe:2022:wmn,
title = {Seamless IoT Mobile Sensing through Wi-Fi Mesh Networking},
author = { Antonio Cilfone and Luca Davoli and Laura Belli and Gianluigi Ferrari},
editor = {Luca Davoli and Gianluigi Ferrari},
doi = {10.1049/PBTE101E_ch4},
year = {2022},
date = {2022-06-15},
urldate = {2022-01-01},
booktitle = {Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications},
pages = {67-80},
publisher = {Institution of Engineering and Technology},
series = {Telecommunications},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Luca Davoli; Gianluigi Ferrari
Conclusions and future perspectives Book Chapter
In: Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications, pp. 265-266, Institution of Engineering and Technology, 2022.
@inbook{dafe:2022:conclusions:wmn,
title = {Conclusions and future perspectives},
author = { Luca Davoli and Gianluigi Ferrari},
doi = {10.1049/PBTE101E_ch13},
year = {2022},
date = {2022-06-15},
urldate = {2022-01-01},
booktitle = {Wireless Mesh Networks for IoT and Smart Cities: Technologies and Applications},
pages = {265-266},
publisher = {Institution of Engineering and Technology},
series = {Telecommunications},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
2021
Luca Davoli; Veronica Mattioli; Sara Gambetta; Laura Belli; Luca Carnevali; Marco Martalò; Andrea Sgoifo; Riccardo Raheli; Gianluigi Ferrari
Non-Invasive Psycho-Physiological Driver Monitoring through IoT-Oriented Systems Book Chapter
In: Pani, Subhendu Kumar; Patra, Priyadarsan; Ferrari, Gianluigi; Kraleva, Radoslava; Wang, Xinheng (Ed.): The Internet of Medical Things: Enabling technologies and emerging applications, pp. 19-33, Institution of Engineering and Technology, London, UK, 2021, ISBN: 9781839532733.
@inbook{damagabecamasgrafe:2021:iomt:bc,
title = {Non-Invasive Psycho-Physiological Driver Monitoring through IoT-Oriented Systems},
author = {Luca Davoli and Veronica Mattioli and Sara Gambetta and Laura Belli and Luca Carnevali and Marco Martalò and Andrea Sgoifo and Riccardo Raheli and Gianluigi Ferrari},
editor = {Subhendu Kumar Pani and Priyadarsan Patra and Gianluigi Ferrari and Radoslava Kraleva and Xinheng Wang},
doi = {10.1049/PBHE034E_ch2},
isbn = {9781839532733},
year = {2021},
date = {2021-12-28},
urldate = {2021-01-01},
booktitle = {The Internet of Medical Things: Enabling technologies and emerging applications},
pages = {19-33},
publisher = {Institution of Engineering and Technology},
address = {London, UK},
abstract = {The definition, analysis, and implementation of in-vehicle monitoring systems that collect data which are informative of the status of the joint driver-vehicle system, represent a topic of strong interest from both academic players and industrial manufacturers. Many external factors, such as road design, road layout, traffic flow and weather can influence and increase driving-related stress, potentially increasing risks. The ubiquitous diffusion of Internet of Things (IoT) technologies allows to collect heterogeneous data that can build the foundation for driver's psycho-physiological characterization, with the aim of improving safety and security while driving. This chapter evaluates and discusses the feasibility and usefulness of a non-invasive IoT-oriented driver monitoring infrastructure aiming at collecting physiological parameters (such as Heart Rate Variability, HRV) that may be adopted as biomarkers of the driver’s psycho-physiological state in different driving scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The definition, analysis, and implementation of in-vehicle monitoring systems that collect data which are informative of the status of the joint driver-vehicle system, represent a topic of strong interest from both academic players and industrial manufacturers. Many external factors, such as road design, road layout, traffic flow and weather can influence and increase driving-related stress, potentially increasing risks. The ubiquitous diffusion of Internet of Things (IoT) technologies allows to collect heterogeneous data that can build the foundation for driver's psycho-physiological characterization, with the aim of improving safety and security while driving. This chapter evaluates and discusses the feasibility and usefulness of a non-invasive IoT-oriented driver monitoring infrastructure aiming at collecting physiological parameters (such as Heart Rate Variability, HRV) that may be adopted as biomarkers of the driver’s psycho-physiological state in different driving scenarios.
Laura Belli; Luca Carnevali; Luca Davoli; Gianluigi Ferrari; Sara Gambetta; Marco Martalò; Veronica Mattioli; Riccardo Raheli; Andrea Sgoifo
Internet of Things per monitorare lo stress dei conducenti: le soluzioni Online
Agenda Digitale 2021, visited: 29.11.2021.
@online{becadafegamamarasg:2021:ad,
title = {Internet of Things per monitorare lo stress dei conducenti: le soluzioni},
author = {Laura Belli and Luca Carnevali and Luca Davoli and Gianluigi Ferrari and Sara Gambetta and Marco Martalò and Veronica Mattioli and Riccardo Raheli and Andrea Sgoifo},
url = {https://www.agendadigitale.eu/cultura-digitale/internet-of-things-per-il-monitoraggio-dello-stress-dei-conducenti-soluzioni-e-scenari/},
year = {2021},
date = {2021-11-29},
urldate = {2021-11-29},
organization = {Agenda Digitale},
abstract = {Presso l’Università di Parma nell’ambito del progetto europeo NextPerception è in fase di sviluppo un sistema di monitoraggio interno ai veicoli volto ad aumentare il grado di sicurezza e di protezione degli occupanti grazie al paradigma dell’Internet of Things. Ecco di cosa si tratta.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
Presso l’Università di Parma nell’ambito del progetto europeo NextPerception è in fase di sviluppo un sistema di monitoraggio interno ai veicoli volto ad aumentare il grado di sicurezza e di protezione degli occupanti grazie al paradigma dell’Internet of Things. Ecco di cosa si tratta.
Laura Belli; Luca Davoli; Gianluigi Ferrari
Internet of Things e intelligenza artificiale, l’unione vincente che cambierà il mondo Online
Agenda Digitale 2021, visited: 29.10.2021.
@online{bedafe:2021:ad,
title = {Internet of Things e intelligenza artificiale, l’unione vincente che cambierà il mondo},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},
url = {https://www.agendadigitale.eu/infrastrutture/internet-of-things-e-intelligenza-artificiale-ununione-vincente-sinergie-e-tendenze/},
year = {2021},
date = {2021-10-29},
urldate = {2021-10-29},
organization = {Agenda Digitale},
abstract = {L’utilizzo congiunto di strumenti Internet of Things e algoritmi di intelligenza artificiale (AIoT) permette di creare sinergia tra mondi che rappresentano il futuro dell’evoluzione tecnologica e abilitare scenari molto eterogenei. Ecco alcuni esempi di utilizzi in cui l’AIoT rappresenta un’integrazione interessante.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
L’utilizzo congiunto di strumenti Internet of Things e algoritmi di intelligenza artificiale (AIoT) permette di creare sinergia tra mondi che rappresentano il futuro dell’evoluzione tecnologica e abilitare scenari molto eterogenei. Ecco alcuni esempi di utilizzi in cui l’AIoT rappresenta un’integrazione interessante.
Laura Belli; Luca Davoli; Gianluigi Ferrari; Emanuele Pagliari
Droni intelligenti e reti mesh: stato dell’arte e sfide tecnologiche Online
Agenda Digitale 2021, visited: 11.10.2021.
@online{bedafepa:2021:ad,
title = {Droni intelligenti e reti mesh: stato dell’arte e sfide tecnologiche},
author = {Laura Belli and Luca Davoli and Gianluigi Ferrari and Emanuele Pagliari},
url = {https://www.agendadigitale.eu/mercati-digitali/droni-intelligenti-e-reti-mesh-stato-dellarte-e-sfide-tecnologiche/},
year = {2021},
date = {2021-10-11},
urldate = {2021-10-11},
organization = {Agenda Digitale},
abstract = {La disponibilità di protocolli di comunicazione eterogenei a bordo di sciami di droni rappresenta un elemento abilitante per scenari molto eterogenei. Una panoramica delle sfide tecnologiche che devono essere affrontate per garantire una comunicazione efficace tra i droni.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
La disponibilità di protocolli di comunicazione eterogenei a bordo di sciami di droni rappresenta un elemento abilitante per scenari molto eterogenei. Una panoramica delle sfide tecnologiche che devono essere affrontate per garantire una comunicazione efficace tra i droni.
Emanuele Pagliari; Luca Davoli; Antonio Cilfone; Gianluigi Ferrari
A Modular Multi-interface Gateway for Heterogeneous IoT Networking Inproceedings
In: 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1-6, Marrakech, Morocco, 2021.
@inproceedings{padacife:2020:isaect,
title = {A Modular Multi-interface Gateway for Heterogeneous IoT Networking},
author = {Emanuele Pagliari and Luca Davoli and Antonio Cilfone and Gianluigi Ferrari},
doi = {10.1109/ISAECT50560.2020.9523689},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)},
pages = {1-6},
address = {Marrakech, Morocco},
abstract = {The massive deployment of Internet of Things (IoT) architectures and applications, in many fields over the last decade, has accelerated research efforts on low-power wireless connectivity protocols. Moreover, many standards have been introduced, highlighting the need to make data flow among different communication protocols and network interfaces feasible. To this end, devices like Gateways (GWs) play a crucial role in many IoT applications and will impact future developments and possibilities. In this paper, the design and deployment of a new modular and scalable GW architecture solution, suitable for a wide plethora of use case scenarios and useful as a starting point for many possible improvements and applications, is proposed. Experimental performance results are discussed, showing the roles of different interfaces in specific use cases in which the proposed this solution may be applied.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The massive deployment of Internet of Things (IoT) architectures and applications, in many fields over the last decade, has accelerated research efforts on low-power wireless connectivity protocols. Moreover, many standards have been introduced, highlighting the need to make data flow among different communication protocols and network interfaces feasible. To this end, devices like Gateways (GWs) play a crucial role in many IoT applications and will impact future developments and possibilities. In this paper, the design and deployment of a new modular and scalable GW architecture solution, suitable for a wide plethora of use case scenarios and useful as a starting point for many possible improvements and applications, is proposed. Experimental performance results are discussed, showing the roles of different interfaces in specific use cases in which the proposed this solution may be applied.
Luca Davoli; Emanuele Pagliari; Gianluigi Ferrari
Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming Journal Article
In: Drones, 5 (2), 2021, ISSN: 2504-446X.
@article{dapafe:2021:drones,
title = {Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming},
author = {Luca Davoli and Emanuele Pagliari and Gianluigi Ferrari},
doi = {10.3390/drones5020026},
issn = {2504-446X},
year = {2021},
date = {2021-06-24},
urldate = {2021-06-24},
journal = {Drones},
volume = {5},
number = {2},
abstract = {Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases.
Gaia Codeluppi; Luca Davoli; Gianluigi Ferrari
Forecasting Air Temperature on Edge Devices with Embedded AI Journal Article
In: Sensors, 21 (12), pp. 1-29, 2021, ISSN: 1424-8220.
@article{codafe:2021:sensors,
title = {Forecasting Air Temperature on Edge Devices with Embedded AI},
author = {Gaia Codeluppi and Luca Davoli and Gianluigi Ferrari},
doi = {10.3390/s21123973},
issn = {1424-8220},
year = {2021},
date = {2021-06-09},
journal = {Sensors},
volume = {21},
number = {12},
pages = {1-29},
abstract = {With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402°C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402°C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.
Professional Activities
Associate Editor
- IET Computers and Digital Techniques, 2022-now
- Frontiers in Communications and Networks – Section “IoT and Sensors Networks,” 2022-now (guest)
- Frontiers in Computer Science – Section “Mobile and Ubiquitous Computing,” 2021-now
Guest Editor
- MDPI Sensors – Special Issue on “Applications of Fog Computing and Edge Computing in IoT Systems,” 2022-2023
- Frontiers in Computer Science – Research Topic on “Design and Development for Next-generation Networks, Internet of Things, Multimedia and Blockchain Technologies,” 2022-2023
- Frontiers in Communications and Networks – Research Topic on “Artificial Intelligence Techniques for Green IoT,” 2022
- [past] MDPI Sensors – Special Issue on “IoT Enabling Technologies for Smart Cities: Challenges and Approaches,” 2021-2022
- [past] MDPI Sensors – Special Issue on “IoT-Based Systems for Smart and Sustainable Agriculture,” 2019
Conference Session/Track Chair
- “General Track – Future IoT and Cloud Computing,” International Conference on Future Internet of Things and Cloud (FiCloud 2022)
- “Edge and In-Network Processing,” IEEE Conference on Network Functions Virtualization and Software-Defined Networking (IEEE NFV-SDN 2021)
- “System Security,” IEEE International Performance Computing and Communications Conference (IPCCC 2021)
Technical Program Committee Member
2023
- IEEE International Conference on Communications (ICC 2023) – IoT & Sensor Networks Symposium
- IFIP Conference on Cloud and Internet of Things (CIoT 2023)
- International Conference on Wireless and Mobile Communications (ICWMC 2023)
- International Conference on Information Networking (ICOIN 2023)
- International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2023)
- International Conference on Ambient Systems, Networks and Technologies (ANT 2023)
- International Conference on Communication Systems & Networks (COMSNETS 2023)
- International Conference on Emerging Data and Industry 4.0 (ED-I40 2023)
- International Conference on Internet of Things, Big Data and Security (IoTBDS 2023)
- Workshop on Privacy Preserving Computation in Pervasive Computing (PrivaCom 2023)
2022
- IEEE International Conference on Communications (ICC 2022) – IoT & Sensor Networks Symposium
- IEEE Conference on Local Computer Networks (LCN 2022)
- IEEE International Conference on Network Protocols (ICNP 2022)
- IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2022)
- IEEE/ACM International Symposium on Quality of Service (IWQoS 2022)
- IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2022)
- IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2022)
- IEEE Annual Computing and Communication Workshop and Conference (CCWC 2022)
- IEEE International Performance Computing and Communications Conference (IPCCC 2022)
- IEEE Symposium on Computers and Communications (ISCC 2022)
- IEEE International Conference on Distributed Computing Systems (ICDCS 2022)
- IEEE International Mediterranean Conference on Communications and Networking (MeditCom 2022)
- IEEE International Conference on Collaboration and Internet Computing (CIC 2022)
- IEEE International Conference on High-Performance Switching and Routing (HPSR 2022)
- IEEE International Conference on Fog and Edge Computing (ICFEC 2022)
- IEEE International Conference on Cloud Networking (CloudNet 2022)
- IEEE International Conference on RFID (RFID 2022)
- IEEE International Conference on Blockchain Computing and Applications (BCCA 2022)
- IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS 2022)
- IEEE International Conference on Smart Internet of Things (SmartIoT 2022)
- IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2022)
- IEEE International Conference on Digital Twin (DigitalTwin 2022)
- IEEE International Conference on Scalable Computing and Communications (ScalCom 2022)
- IEEE World Forum on Internet of Things (WF-IoT 2022)
- IEEE Conference on Technologies for Sustainability (SusTech 2022)
- IEEE International Conference on Big Data (BigData 2022)
- IEEE International Conference on Dependable, Autonomic & Secure Computing (DASC 2022)
- IEEE International Conference on Mobile And Secure Services (MobiSecServ 2022)
- IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2022)
- IEEE International Conference on Intelligent Data and Security (IDS 2022)
- IEEE International Smart Cities Conference (ISC2 2022)
- IEEE International Conference on Smart Computing for Smart Cities (SC2 2022)
- IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2022)
- IEEE Cloud Summit 2022
- IEEE International Conference on Internet of Things and Intelligence System (IoTaIS 2022)
- IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT 2022)
- IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS 2022)
- IEEE International Conference on Computer Communications and Networks (ICCCN 2022)
- IEEE International Conference on Network and Service Management (CNSM 2022)
- IEEE International Conference on Internet of Things (iThings 2022)
- IEEE IoT Vertical and Topical Summit for Tourism (IoT-VTST 2022)
- IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2022)
- IEEE Information Technology International Seminar (ITIS 2022)
- ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS 2022)
- ACM Multimedia Systems Conference (MMSys 2022)
- ACM/SIGAPP Symposium On Applied Computing (SAC 2022) – Special Track on Internet of Things
- ACM International Workshop on Blockchain-enabled Networked Sensor Systems (BlockSys 2022)
- ITU Kaleidoscope 2022: Extended reality
- IFIP Wireless and Mobile Networking Conference (IFIP WMNC 2022)
- IFIP Conference on Cloud and Internet of Things (CIoT 2022)
- IFIP Working Conference on the Practice of Enterprise Modelling (PoEM 2022)
- IFIP Networking 2022
- International Conference on Network of the Future (NoF 2022)
- International Workshop on Security, Privacy, Trust for Internet of Things (IoTSPT 2022)
- International SenSys/BuildSys Workshop on Data (DATA 2022)
- International Conference on Embedded Wireless Systems and Networks (EWSN 2022)
- International Workshop on Artificial Intelligence and Industrial Internet-of-Things Security (AIoTS 2022)
- Future Internet Services and Applications (FISA 2022)
- International Conference on ICT Convergence (ICTC 2022)
- International Conference on Ubiquitous Networking (UNet 2022)
- International Conference on Mobility, Sensing and Networking (MSN 2022)
- International workshop on e-Health Pervasive Wireless Applications and Services (e-HPWAS 2022)
- International Conference on Ubiquitous Security (UbiSec 2022)
- International Conference on Wireless and Mobile Communications (ICWMC 2022)
- International Conference on Smart Computing (SMARTCOMP 2022)
- International Conference on Advances in Mobile Computing & Multimedia Intelligence (MoMM 2022)
- International Conference on Networks, Communications and Information Technology (CNCIT 2022)
- Workshop on CyberPhysical Systems for Emergency Response (CPS-ER 2022)
- International Workshop on Business Models and Techno-economic of 5G Networks and Beyond (BMTE5G 2022)
- International Workshop on Security, Privacy and Trust in the Internet of Things (SPT-IoT 2022)
- International Conference on Communication and Network Technology (ICCNT 2022)
- Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices (WristSense 2022)
- International Workshop on Cooperative Wireless Networks (CWN 2022)
- International Workshop on Artificial Intelligence and Industrial Internet-of-Things Security (AIoTS 2022)
- International Conference on Emerging Data and Industry 4.0 (ED-I40 2022)
- International Conference on Future Networks and Communications (FNC 2022)
- International Conference on Big Data and Internet of Things (BDIOT2022)
- International Conference on the Internet of Things (IoT 2022)
- International Conference on Innovations and Development of Information Technologies and Robotics (IDITR 2022)
- International Conference on Information Communication and Applications (ICICA 2022)
- International Conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies (ICCCSET 2022)
- International Workshop on Digital Twin Engineering (DT Engineering 2022)
- International Conference on Communication Systems & Networks (COMSNETS 2022)
- International Conference on Internet of Things and Intelligent Applications (ITIA 2022)
- International Conference of Smart Systems and Emerging Technologies (SmartTech 2022)
- European Conference on Service-Oriented and Cloud Computing (ESOCC 2022)
- Conference on Innovation in Clouds, Internet and Networks (ICIN 2022)
- International Fintech Congress (IFC 2022)
2021
- IEEE Global Communications Conference (GLOBECOM 2021) – WS-05: Workshop on Softwarized Next Generation Networks for IoT Services
- IEEE Global Communications Conference (GLOBECOM 2021) – WS-06: Workshop on Edge-AI and IoT for Connected Health
- IEEE Global Communications Conference (GLOBECOM 2021) – WS-06: WS-18: Workshop on Securing Next-Generation Connected Healthcare Systems using Futuristic Technologies
- IEEE/ACM Conference on Connected Health Applications, Systems, and Engineering Technologies (CHASE 2021)
- IEEE Conference on Local Computer Networks (LCN 2021)
- IEEE Conference on Network Functions Virtualization and Software-Defined Networking (NFV-SDN 2021)
- IEEE International Conference on Collaboration and Internet Computing (CIC 2021)
- IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2021)
- IEEE International Conference on Big Data (BigData 2021)
- IEEE International Symposium on Network Computing and Applications (NCA 2021)
- IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD 2021)
- IEEE Cloud Summit 2021
- IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS 2021)
- IEEE International Conference on ICT Convergence (ICTC 2021)
- IEEE Symposium on Future Telecommunication Technologies (SOFTT 2021)
- IEEE International Multidisciplinary Conference on Engineering Technology (IMCET 2021)
- IEEE International Workshop on Distributed and Intelligent Systems (DistInSys 2021)
- IEEE International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI (HONET 2021)
- IEEE Future Networks World Forum – 5G World Forum (5G-WF 2021)
- IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT 2021)
- IEEE International Conference on Internet of Things and Intelligence System (IoTaIS 2021)
- IEEE International Workshop on Computing and Communication Technologies for Internet of Things (IWCCT 2021)
- IEEE International Conference on Pervasive Intelligence and Computing (PICom 2021)
- IEEE International Performance Computing and Communications Conference (IPCCC 2021)
- IEEE International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2021)
- IEEE International Conference on Mobility, Sensing and Networking (MSN 2021)
- IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2021)
- IEEE International Conference on Smart and Sustainable Technologies (SpliTech 2021)
- IEEE International Conference on Smart City Innovations (SCI 2021)
- IEEE International Workshop on Latest Advances in Enterprise Architectures in the IoT Era (EAIoT 2021)
- IEEE International Conference on Big Data Computing Service and Machine Learning Applications (Big Data Service 2021)
- IEEE International Conference on Network and Service Management (CNSM 2021)
- IEEE Smart World Congress (Smart World 2021)
- IEEE International Conference on Smart Internet of Things (SmartIoT 2021)
- IEEE Symposium on Computers and Communications (ISCC 2021)
- IEEE International Conference on Blockchain Computing and Applications (BCCA 2021)
- IEEE IoT Vertical and Topical Summit for Tourism (IoT-VTST 2021)
- IEEE International Smart Cities Conference (ISC2 2021)
- ITU Kaleidoscope 2021: Connecting physical and virtual worlds
- ACM International Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet 2021)
- ACM International Workshop on Blockchain-enabled Networked Sensor Systems (BlockSys 2021)
- ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW 2021)
- EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2021)
- International Conference On Advances In Cyber Security (ACES 2021)
- International Conference on Wireless and Mobile Communications (ICWMC 2021)
- International Workshop on E-Health Pervasive Wireless Applications and Services (e-HPWAS 2021)
- International Conference on Data Science and Computational Intelligence (DSCI 2021)
- International Workshop on Cloud and Edge Computing, and Applications Management (CloudAM 2021)
- Workshop on Mobility Support in Slice-based Network Control for Heterogeneous Environments (MOBISLICE 2021)
- International Conference on Information, Communication & Cybersecurity (ICI2C 2021)
- International Workshop on IoT Applications and Industry 4.0 (IoTI4 2021)
- International Conference on the Internet of Things (IoT 2021)
- International Workshop on Cooperative Wireless Networks (CWN 2021)
- International Conference on Ubiquitous Security (UbiSec 2021)
- International Conference on Software Defined Systems (SDS 2021)
2019
- International Conference on Intelligent Computing & Smart Communication (ICSC 2019)
2018
- International Conference on Data and Smart Systems Engineering (DSSE 2018)
2017
- International Conference on Intelligent Information Technologies (ICIIT 2017)
- International Conference on Advanced Wireless Information, Data, and Communication Technologies (AWICT 2017)
- International Conference on Internet of Things and Machine Learning (IML 2017)
Frequent Reviewer
- Journals: IEEE/ACM Transactions on Networking, IEEE Transactions on Industrial Informatics, IEEE Transactions on Biomedical Engineering, IEEE Internet of Things Journal, IEEE Network, IEEE Communications Letters, IEEE Communications Standards Magazine, IEEE Networking Letters, IEEE Internet Computing, IEEE Access, IEEE Sensors Journal, IEEE Internet of Things Magazine, IEEE Software Magazine, IEEE Journal on Selected Areas in Communications – Series on Green Communications and Networking, Elsevier Computer Networks, Elsevier Computer Communications, Elsevier Future Generation Computer Systems, Elsevier ICT Express, Elsevier Pervasive and Mobile Computing, Elsevier Soil Security, Elsevier Information Processing in Agriculture, Springer Journal of Ambient Intelligence and Humanized Computing, Springer Telecommunication Systems, Wiley International Journal of Network Management, Springer Wireless Networks, Springer Computing, MDPI Future Internet, MDPI Information, MDPI Applied Sciences, MDPI Computers, MDPI Sensors, MDPI Processes, MDPI Symmetry, MDPI Smart Cities, MDPI Electronics, MDPI Sustainability, IET Smart Cities, Connection Science, Intelligenza Artificiale, Journal of Ambient Intelligence and Smart Environments, Circuit World, Computer Science and Information Systems, KSII Transactions on Internet and Information Systems, Springer Journal of Cloud Computing, Springer IEIB – Journal of The Institution of Engineers (India): Series B, Agronomy Research, ASTM Journal of Testing and Evaluation, Hindawi Wireless Communications and Mobile Computing, Hindawi Mobile Information Systems, Hindawi Journal of Computer Networks and Communications, Hindawi International Journal of Distributed Sensor Networks, International Journal of Simulation and Process Modelling, International Journal of Electrical and Electronic Engineering & Telecommunications, International Journal of Environment and Sustainable Development, International Journal of Information Technology and Management, International Journal of Testing and Evaluation, International Journal of Ambient Energy, Journal of Unmanned Vehicle Systems, Journal of Current Science and Technology, and others.
- Conferences: IEEE International Conference on Communications (ICC), IEEE Global Communications Conference (GLOBECOM), IEEE Sensors, IEEE Global Power, Energy and Communication Conference (GPECOM), IEEE International Workshop on High Performance Computing and Collaborative Networking for Agricultural Applications (AgrApps), IEEE International Conference on Software, Telecommunications and Computer Networks (SoftCOM), IEEE International Conference on Electronics Circuits and Systems (ICECS), IEEE International Symposium on Wireless Communication Systems (ISWCS), IEEE International Conference on Selected Topics in Mobile and Wireless Networking (MoWNet), IEEE International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE Consumer Communications & Networking Conference (CCNC), IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), IEEE International Conference on Information Networking (ICOIN), IEEE International Conference on Wireless Communications and Signal Processing (WCSP), IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE International Conference on Signals and Systems (ICSigSys), IEEE International Symposium on Ubiquitous Networking (UNet), IEEE International Symposium on Power Line Communications and its Applications (ISPLC), IEEE International Conference on Wireless Networks and Mobile Communications (WINCOM), IEEE International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI (HONET), IEEE Symposium on Emerging Topics in Computing and Communications (SETCAC), IEEE International Conference on Computing and Network Communications (CoCoNet), International Conference on Safety & Environment in Process & Power Industry (CISAP), and others.
Teaching & Students
Teaching (“Didattica”)
2024-2025
- Vehicular Communications class @ MUNER – (Elly Website)
- Reti di Telecomunicazione e Laboratorio class @ UniPR – (Elly Website)
2023-2024
- Vehicular Communications class @ MUNER – (Elly Website)
- Reti di Telecomunicazione e Laboratorio class @ UniPR – (Elly Website)
2022-2023
- Telematica class @ UniPR – (Elly Website)
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2021-2022
- Telematica class @ UniPR – (Elly Website)
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2020-2021
- Adjunct Professor for Telematica class @ UniPR – (Elly Website)
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2019-2020
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2018-2019
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2017-2018
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Lecturer for “Internet of Things” and “Predictive Maintenance” classes @ Forma Futuro (Parma, IT)
- Lecturer for “Sistemi IoT” class @ Cisita Parma (Parma, IT)
2016-2017
- Assistant Lecturer for Internet of Things class @ UniPR
Lecturer: Prof. Gianluigi Ferrari - Summer TLC stage for university orientation @ UniPR (taught in Italian)
- Assistant Lecturer for Sistemi IoT class @ Cisita Parma (Parma, IT)
Lecturer: Prof. Gianluigi Ferrari
2015-2016
- Assistant Lecturer for Reti di Telecomunicazione class, Software Defined Networking (SDN) lectures
Lecturer: Prof. Luca Veltri - Assistant Lecturer for Internet of Things class
Lecturer: Prof. Gianluigi Ferrari - Summer TLC Stage for university orientation @ UniPR (taught in Italian)
2014-2015
- Assistant Lecturer for Internet of Things class
Lecturer: Prof. Gianluigi Ferrari
2012-2013
- Assistant Lecturer for Electronics Computers class
Lecturer: Prof. Gianni Conte
Supervised students
Master Students
- Shyaka Jean Leon Benin Ngenzi – Edge-oriented Image Processing-based Parking Space Identification Using Machine Learning for Constrained Networks – March 2025
Advisor: Prof. Ing. Gianluigi Ferrari - Bahman Hatami – Federated Learning for EV Range Prediction under Diverse Speed and Terrain Conditions – March 2025
Advisor: Prof. Ing. Gianluigi Ferrari - Marco Sanfelici – Analysis and optimization of an IoT system for real time capture of inertial and position data – December 2024
Advisor: Prof. Ing. Gianluigi Ferrari - Ralph Akiki – Implementation of a Master-Slave IoT Communication Protocol for Smart Building Applications – December 2024
Advisor: Prof. Ing. Gianluigi Ferrari - Luca Taverna – Multi-Interface Gateway with Embedded Intelligence: Design and Deployment – July 2024
Advisor: Prof. Ing. Gianluigi Ferrari - Giulia Oddi – Design and implementation of an IoT data collection and analysis platform for smart agriculture – December 2023
Advisor: Prof. Ing. Gianluigi Ferrari - Fabio Freddi – Analysis of Wearable IoT Node-based Movement Data – December 2023
Advisor: Prof. Ing. Gianluigi Ferrari - Daniele Antonucci – Embedding Intelligence in IoT Nodes for Air Quality Prediction – October 2022
Advisor: Prof. Ing. Gianluigi Ferrari - Omer Babiker Ali Mohamed Elamin – Design of a Cloud-oriented Smart Waste Management System for Future Smart Cities – July 2022
Advisor: Prof. Ing. Luca Davoli - Behrang Mahmoudi – Distributed Air Quality Monitoring Architecture for Heterogeneous Scenarios – July 2021
Advisor: Prof. Ing. Gianluigi Ferrari - Emanuele Pagliari – Design and Development of a Modular Multi-interface Gateway for Heterogeneous IoT Networking – July 2020
Advisor: Prof. Ing. Gianluigi Ferrari - Jodi Oxoli – Ultrasonic Sensor-Based Environmental Perception – March 2020
Advisor: Prof. Ing. Gianluigi Ferrari - Sunil Mathew – Low Latency Digital Audio Wireless Networking – March 2020
Advisor: Prof. Ing. Gianluigi Ferrari - Roberto Pasquali – Design and Implementation of a Cloud-based Architecture for Machine-Driven Maintenance – December 2018
Advisor: Prof. Ing. Michele Amoretti - Gaia Codeluppi – VegIoT Garden: Design and Implementation of an IoT Platform to Collect, Display and Analyze Sensory Data of a Urban Vegetable Garden – October 2018
Advisor: Prof. Ing. Gianluigi Ferrari - Alessandro Alinovi – Design and Implementation of a Sink-Oriented Routing Protocol for BLE Networks – March 2018
Advisor: Prof. Ing. Gianluigi Ferrari - Alessandro Nicoli – Design and Implementation of an IoT Monitoring System for Precision Agriculture – December 2016
Advisor: Prof. Ing. Gianluigi Ferrari - Antonio Cilfone – Integration of Memory Constrained Mobile Wi-Fi Nodes in IoT Scenarios: Design and Implementation – March 2016
Advisor: Prof. Ing. Gianluigi Ferrari
Bachelor Students
- Leonardo Bacciocchi – Design of a facial recognition pipeline through a thermographic face mapping – March 2025
Advisor: Prof. Ing. Luca Davoli - Lorenzo Apolone – Design and Development of an IoT System for Automated Irrigation in Smart Agriculture Scenarios – March 2025
Advisor: Prof. Ing. Luca Davoli - Matteo Corsano – CardinalAPI: Design and Development of a Node.js-based IoT Infrastructure for Smart Agriculture – March 2025
Advisor: Prof. Ing. Luca Davoli - Giacomo Patelli – Cloud Journey> Cloud Migration of a Bank-s IT Systems – December 2024
Advisor: Prof. Ing. Luca Davoli - Mattia Rainieri – ClubPass: Design and Development of a Management System for Public Entertainment Clubs – October 2024
Advisor: Prof. Ing. Luca Davoli - Chiara Reggiani – Vehicular Communication and Video Streaming for Autonomous Competition Vehicles – July 2024
Advisor: Prof. Ing. Alessandro Ugolini - Gianluca Altomani – Analysis and Exploitation of a Security Camera through Reverse Engineering – March 2024
Advisor: Prof. Ing. Luca Davoli - Manuel Carini – Integration of C#, Microsoft Azure and SQL Technologies for an Efficient Real-Time Data Forwarding and Management – December 2023
Advisor: Prof. Ing. Luca Davoli - Silvio Ronca – Design and Development of a Software Tool for the Consumption Optimization and Prediction of a Cogenerator – October 2023
Advisor: Prof. Ing. Luca Davoli - Mirko Piazza – Impact of Digitalization on the Banking Sector: The Key Role of Cloud Computing as a Driver of Change – July 2023
Advisor: Prof. Ing. Luca Davoli - Edoardo Sichelli – Design and Deployment of a Web-based UI for IoT-enabled Air Quality Monitoring in Heterogeneous Scenarios – July 2023
Advisor: Prof. Ing. Luca Davoli - Giacomo Domeniconi – Design and Deployment of a Decision Support System (DSS) with User Interface for Smart Agriculture Scenarios – July 2023
Advisor: Prof. Ing. Luca Davoli - Filipp Dassoni – Design and Development of an Arduino-based Air Quality Monitoring Node – March 2023
Advisor: Prof. Ing. Luca Davoli - Alex Mulder – Implementation of BLE Communications for Exoskeletons in Medical Field – March 2023
Advisor: Prof. Ing. Luca Davoli - Andrea Oppici – Design and Development of a MQTT-based Telemetry System for Electric Competition Vehicles – December 2022
Advisor: Prof. Ing. Luca Davoli - Andrea Lucato – Design and Development of a Backoffice Bill of Materials Module for a MES Software – December 2022
Advisor: Prof. Ing. Luca Davoli - Gabriele Fagnoni – Development of a Desktop Application for the Configuration and Data Collection from an IoT Wearable Device through BLE Protocol – December 2022
Advisor: Prof. Ing. Luca Davoli - Luca Albano – Design and Implementation of an IoT Node for Air Quality Monitoring – December 2022
Advisor: Prof. Ing. Gianluigi Ferrari - Eni Zeza – Design and Development of a Flutter-based Mobile Application for Smart Meeting Management – October 2022
Advisor: Prof. Ing. Luca Davoli - Martina Dominici – Design and Deployment of a Web-based Application for the Management of a Gym – October 2022
Advisor: Prof. Ing. Luca Davoli - Davide Alzeti – Inertial Data Collection and Analysis for Vehicle Driving Monitoring – December 2020
Advisor: Prof. Ing. Marco Martalò - Marco Chiesa – Development of a Remote Consumption Monitoring System with Integration of the Modbus and LoRaWAN protocols – December 2020
Advisor: Prof. Ing. Gianluigi Ferrari - Amedeo Giuliani – Experimental Analysis of GNSS-based Positioning Accuracy in Mobile Devices – July 2020
Advisor: Prof. Ing. Gianluigi Ferrari - Fabio Freddi – Design and Implementation of a Wearable System for Real Time Athlete Performance Analysis and Injury Prevention – December 2019
Advisor: Prof. Ing. Gianluigi Ferrari - Giulia Bussi – Design of a Qt Application for the Selection and Categorization of Parking Areas – October 2019
Advisor: Prof. Ing. Gianluigi Ferrari - Davide Draghi – Design and development of a LoRa protocol-based Telemetry System for electrical Formula SAE vehicles – July 2019
Advisor: Prof. Ing. Gianluigi Ferrari - Tommaso Gardoni – Smartphone-based Analysis of the Channel Status in LTE Cellular Networks – March 2019
Advisor: Prof. Ing. Gianluigi Ferrari - Behrang Mahmoudi – UAV-Based Sub-GHz Communication Systems – March 2018
Advisor: Prof. Ing. Gianluigi Ferrari - Emanuele Pagliari – Interaction Mechanisms between Bluetooth Low Energy (BLE) Devices – December 2017
Advisor: Prof. Ing. Gianluigi Ferrari - Alessandro Fragalà – Design and Implementation of a Telemetry system for an EFI Euro4 Engine Control Unit – October 2017
Advisor: Prof. Ing. Gianluigi Ferrari - Andrea Benassi – Design and Development of a BLE System for Proximity-based Applications – December 2016
Advisor: Prof. Ing. Gianluigi Ferrari - Alessandro Manfredi – Design and Implementation of a Real-Time Telemetry System for Competition Vehicles – October 2016
Advisor: Prof. Ing. Gianluigi Ferrari - Luca Cirani – Confidenzialità delle comunicazioni M2M tra nodi a ridotta capacità computazionale su reti IPv6 – July 2016
Advisor: Prof. Roberto Alfieri - Mattia Pizzoni – Design and Development of an Internet of Things Multi-Sensor Surveillance System – March 2016
Advisor: Prof. Ing. Marco Picone - Fabrizio Carpi – Experimental Study of SDN Network Architectures – December 2015
Advisor: Prof. Ing. Luca Veltri - Riccardo Castellini – Experimental Study of Software-Defined Networking based on Mininet simulator – October 2015
Advisor: Prof. Ing. Luca Veltri - Nicolò Porcari – Applying the MQTT protocol and Mobile Computing in Internet of Things Scenarios – December 2014
Advisor: Prof. Ing. Simone Cirani - Tuan Vu Hong Bao – Design and Implementation of a MQTT-based telemetry system with Android devices – December 2014
Advisor: Prof. Ing. Simone Cirani