Nuova ricerca

Luca BEDOGNI

Professore Associato
Dipartimento di Scienze Fisiche, Informatiche e Matematiche sede ex-Matematica

Home | Curriculum(pdf) | Didattica |


Pubblicazioni

2024 - Digital Twins & Fluid Computing in the Edge-to-Cloud Compute Continuum [Relazione in Atti di Convegno]
Picone, Marco; Bedogni, Luca; Pietri, Marcello; Mamei, Marco; Zambonelli, Franco
abstract

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

2024 - Fluid Computing in the Internet of Things: A Digital Twin Approach [Relazione in Atti di Convegno]
Bedogni, Luca; Picone, Marco; Pietri, Marcello; Mamei, Marco; Zambonelli, Franco
abstract

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

2024 - Towards Coordinating Machines and Operators in Industry 5.0 through the Web of Things [Relazione in Atti di Convegno]
Picone, Marco; Villani, Valeria; Pietri, Marcello; Bedogni, Luca
abstract

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

2024 - Towards Operator Digital Twins in Industry 5.0: Design Strategies & Experimental Evaluation [Relazione in Atti di Convegno]
Picone, Marco; Morandi, Riccardo; Villani, Valeria; Pietri, Marcello; Bedogni, Luca
abstract

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

2023 - A Joint Evaluation Methodology for Service Quality and User Privacy in Location Based Systems [Relazione in Atti di Convegno]
Bedogni, L.; Franceschini, C.; Montori, F.
abstract

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

2023 - Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations [Relazione in Atti di Convegno]
Zarfati, E.; Bedogni, L.
abstract

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

2023 - Computation Efficient ECG Classification on Resource Constrained Devices [Relazione in Atti di Convegno]
Arigliano, A.; Malagoli, A.; Bedogni, L.
abstract

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

2023 - Does the venue of scientific conferences leverage their impact? A large scale study on Computer Science conferences [Articolo su rivista]
Bedogni, L.; Cabri, G.; Martoglia, R.; Poggi, F.
abstract

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

2023 - GreenCrowd: Toward a Holistic Algorithmic Crowd Charging Framework [Articolo su rivista]
Raptis, T. P.; Bedogni, L.
abstract

Crowd charging represents an alternative peer-to-peer energy replenishment option for mobile users to align with the circular economy paradigm. Following this option, users bound by finite resource capacity utilize the energy from external to the crowd wireless or wired energy sources (such as shared chargers), and internal to the crowd energy sources (such as mobile devices, via wireless power transfer). If designed carefully, such utilization can boost the energy availability of users and provide energy ubiquitously to their devices for making them functional for longer. This article proposes the GreenCrowd framework, introducing a privacy-by-design in the digital domain crowd charging process, the architecture of which incorporates multiple crowd-* components, such as online social information exploitation, algorithmic battery aging mitigation, user reward mechanisms, and advanced decision making. The primary aim of article is to present the technological and applicative requirements and constraints of GreenCrowd, and provide practical evidence on its feasibility.

2023 - Joint privacy and data quality aware reward in opportunistic Mobile Crowdsensing systems [Articolo su rivista]
Bedogni, L; Montori, F
abstract

Mobile Crowdsensing (MCS) is a paradigm involving a crowd of participants, called workers, into sensor data gathering campaigns through their personal devices. Some campaigns require workers to contribute with small amounts of geolocalized data at a constant rate, while being not directly aware of the global conditions of the system. In the scope of this reduced awareness, it is crucial to consider the privacy preservation of single workers at design time, as the disclosure of their exact location may lead to severe privacy issues. In this paper we design a privacy by design MCS framework that leverages variable rewards for workers willing to submit their location with an higher precision than others. Privacy is ensured through a negotiation phase that estimates the reward of the workers for different levels of location precision. This way, it helps them decide autonomously the spatial granularity of their data in order to preserve their privacy, yet obtaining a reward for their data. We design a metric based on k-anonymity to evaluate the level of privacy achieved, and validate the proposed framework over a real dataset. Our results show the efficacy of the framework as well as interesting effects caused by the topology of the environment.

2023 - Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenarios [Articolo su rivista]
Montori, Federico; Bedogni, Luca
abstract

2023 - Texting and Driving Recognition leveraging the Front Camera of Smartphones [Relazione in Atti di Convegno]
Montori, F.; Spallone, M.; Bedogni, L.
abstract

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

2023 - Towards User Behavior Forecasting in Mobile Crowdsensing Applications [Relazione in Atti di Convegno]
Bedogni, L.; Buferli, M.; Marchi, D.
abstract

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

2022 - A Hierarchical Architectural Model for IoT End-User Service Composition [Relazione in Atti di Convegno]
Montori, F.; Armandi, V.; Bedogni, L.
abstract

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

2022 - A Web Of Things Context-Aware IoT System leveraging Q-learning [Relazione in Atti di Convegno]
Bedogni, L.; Poggi, F.
abstract

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

2022 - Enabling Green Crowdsourced Social Delivery Networks in Urban Communities † [Articolo su rivista]
Choi, K.; Bedogni, L.; Levorato, M.
abstract

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

2022 - Location Contact Tracing: Penetration, Privacy, Position, and Performance [Articolo su rivista]
Bedogni, L.; Montori, F.; Salim, F.
abstract

2022 - Pedometers for Smartphones: Analysis and Comparison of Real-Time Algorithms [Relazione in Atti di Convegno]
Neri, G.; Montori, F.; Gigli, L.; Bedogni, L.; Di Felice, M.; Bononi, L.
abstract

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

2022 - Re-identification Attack based on Few-Hints Dataset Enrichment for Ubiquitous Applications [Relazione in Atti di Convegno]
Artioli, A.; Bedogni, L.; Leoncini, M.
abstract

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

2022 - SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems [Relazione in Atti di Convegno]
Amiri, D.; Takalo-Mattila, J.; Bedogni, L.; Levorato, M.; Dutt, N.
abstract

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

2022 - WISE: A Semantic and Interoperable Web of Things Architecture for Smart Environments [Relazione in Atti di Convegno]
Bedogni, L.; Manfredini, S.; Poggi, F.; Rossi, D.
abstract

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

2021 - Anomaly Detection and Classification in Predictive Maintenance Tasks with Zero Initial Training [Articolo su rivista]
Morselli, Filippo; Bedogni, Luca; Mirani, Umberto; Fantoni, Michele; Galasso, Simone
abstract

The Fourth Industrial Revolution has led to the adoption of novel technologies and methodologies in factories, making these more efficient and productive. Among the new services which are changing industry, there are those based on machine learning algorithms, which enable machines to learn from their past observations and hence possibly forecast future states. Specifically, predictive maintenance represents the opportunity to understand in advance possible machine outages due to broken parts and schedule the necessary maintenance operations. However, in real scenarios predictive maintenance struggles to be adopted due to a multitude of variables and the heavy customization it requires. In this work, we propose a novel framework for predictive maintenance, which is trained online to recognize new issues reported by the operators. Our framework, tested on different scenarios and with a varying number and several kinds of sensors, shows recall levels above 0.85, demonstrating its effectiveness and adaptability.

2021 - IoT End-User Service Composition via a Visual Programming Interface [Relazione in Atti di Convegno]
Montori, F.; Armandi, V.; Bedogni, L.
abstract

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

2021 - Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications [Articolo su rivista]
Bedogni, Luca; Rumi, Shakila Khan; Salim, Flora D.
abstract

2020 - A Privacy Preserving Framework for Rewarding Users in Opportunistic Mobile Crowdsensing [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca
abstract

2020 - Delivering IoT Smart Services through Collective Awareness, Mobile Crowdsensing and Open Data [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca; Iselli, Gianluca; Bononi, Luciano
abstract

2020 - Identification of Social Aspects by Means of Inertial Sensor Data [Articolo su rivista]
Bedogni, Luca; Cabri, Giacomo
abstract

2020 - Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator [Articolo su rivista]
Montori, Federico; Bedogni, Luca; Fiandrino, Claudio; Capponi, Andrea; Bononi, Luciano
abstract

2020 - Special issue on “Crowd-sensed Big Data for Internet of Things Services” [Articolo su rivista]
Bedogni, L.; Kanhere, S.; Wu, H.; Bononi, L.
abstract

2020 - Towards Green Crowdsourced Social Delivery Networks: A Feasibility Study [Relazione in Atti di Convegno]
Choi, K.; Bedogni, L.; Levorato, M.
abstract

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

2020 - Welcome from the IoTSenCity 2020 Workshop Organizers [Relazione in Atti di Convegno]
Montori, F.; Bedogni, L.; Jayaraman, P. P.
abstract

2019 - CrowdSensim 2.0: A stateful simulation platform for mobile crowdsensing in smart cities [Relazione in Atti di Convegno]
Montori, F.; Cortesi, E.; Bedogni, L.; Capponi, A.; Fiandrino, C.; Bononi, L.
abstract

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

2019 - Dynamic spectrum access for machine to machine communications: Opportunities, standards, and open issues [Capitolo/Saggio]
Bedogni, L.; Di Felice, M.; Bononi, L.
abstract

Cognitive radio can be applied to a multitude of domains, one of which is M2M communication. Specifically, M2M communication refers to communication between devices without human intervention. Hence, devices should be able to organize themselves and run the communication protocol autonomously. If cognitive radio is used, tasks such as dynamic spectrum access (DSA), spectrum sensing, and alike present additional challenges compared to traditional network, as all the decision framework should be implemented and automatized in the devices. In this chapter, we focus on DSA techniques for M2M. The main difference from other kinds of communication is relative both to the energy efficiency and to the low protocol overhead, as devices should run for long periods of time and run without human intervention. At first we present related work from literature, categorizing the different tasks devices which want to leverage DSA on M2M have to perform. At the end of the chapter, we present a proof of concept of a general framework, which can be applied to different scenario concerning M2M, encompassing all the spectrum management and measurement tasks M2M devices should generally perform. Finally, we derive open challenges and future research directions concerning this scenario.

2019 - Permission-free Keylogging through Touch Events Eavesdropping on Mobile Devices [Relazione in Atti di Convegno]
Bedogni, L.; Alcaras, A.; Bononi, L.
abstract

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

2019 - Reinforcement learning-based spectrum management for cognitive radio networks: A literature review and case study [Capitolo/Saggio]
Di Felice, M.; Bedogni, L.; Bononi, L.
abstract

In cognitive radio (CR) networks, the cognition cycle, i.e., the ability of wireless transceivers to learn the optimal configuration meeting environmen- tal and application requirements, is considered as important as the hardware components which enable the dynamic spectrum access (DSA) capabilities. To this purpose, several machine learning (ML) techniques have been applied on CR spectrum and network management issues, including spectrum sensing, spectrum selection, and routing. In this paper, we focus on reinforcement learning (RL), an online ML paradigm where an agent discovers the optimal sequence of actions required to perform a task via trial-end-error interactions with the environment. Our study provides both a survey and a proof of concept of RL applications in CR networking. As a survey, we discuss pros and cons of the RL framework compared to other ML techniques, and we provide an exhaustive review of the RL-CR literature, by considering a twofold perspective, i.e., an applicationdriven taxonomy and a learning methodology-driven taxonomy. As a proof of concept, we investigate the application of RL techniques on joint spectrum sensing and decision problems, by comparing different algorithms and learning strategies and by further analyzing the impact of information sharing techniques in purely cooperative or mixed cooperative/competitive tasks.

2019 - Texting and driving recognition exploiting subsequent turns leveraging smartphone sensors [Relazione in Atti di Convegno]
Bedogni, L.; Bujor, O.; Levorato, M.
abstract

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

2019 - Vehicular Route Identification Using Mobile Devices Integrated Sensors [Relazione in Atti di Convegno]
Bedogni, L.; Bononi, L.
abstract

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

2018 - A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring [Articolo su rivista]
Montori, Federico; Bedogni, Luca; Bononi, Luciano
abstract

2018 - Custom Dual Transportation Mode Detection By Smartphone Devices Exploiting Sensor Diversity [Relazione in Atti di Convegno]
Carpineti, Claudia; Lomonaco, Vincenzo; Bedogni, Luca; Felice Marco, Di; Bononi, Luciano
abstract

2018 - Dual-mode wake-up nodes for IoT monitoring applications: Measurements and algorithms [Relazione in Atti di Convegno]
Bedogni, Luca; Bononi, Luciano; Canegallo, Roberto; Carbone, Fabio; Di Felice, Marco; Scarselli Eleonora, Franchi; Montori, Federico; Perilli, Luca; Cinotti Tullio, Salmon; Trotta, Angelo
abstract

2018 - Machine-to-machine wireless communication technologies for the Internet of Things: Taxonomy, comparison and open issues [Articolo su rivista]
Montori, Federico; Bedogni, Luca; Di Felice, Marco; Bononi, Luciano
abstract

2018 - PerCom Workshops 2018 Committees [Relazione in Atti di Convegno]
Bedogni, L.; Restuccia, F.
abstract

2018 - Rising User Privacy Against Predictive Context Awareness Through Adversarial Information Injection [Relazione in Atti di Convegno]
Bedogni, Luca; Levorato, Marco
abstract

2018 - Temporal Reachability in Vehicular Networks [Relazione in Atti di Convegno]
Bedogni, Luca; Fiore, Marco; Glacet, Christian
abstract

2018 - WiFi Meets Barometer: Smartphone-Based 3D Indoor Positioning Method [Relazione in Atti di Convegno]
Bisio, Igor; Sciarrone, Andrea; Bedogni, Luca; Bononi, Luciano
abstract

2017 - Achieving IoT Interoperability through a Service Oriented In-Home Appliance [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca; Morselli, Filippo; Bononi, Luciano
abstract

2017 - Automotive Communications in LTE: a Simulation-based Performance Study [Relazione in Atti di Convegno]
Montori, F.; Gramaglia, M.; Bedogni, L.; Fiore, M.; Sheikh, F.; Bononi, L.; Vesco, A.
abstract

2017 - Distributed data collection control in opportunistic mobile crowdsensing [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca; Bononi, Luciano
abstract

2017 - Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks [Articolo su rivista]
Bedogni, Luca; Trotta, Angelo; Di Felice, Marco; Gao, Yue; Zhang, Xingjian; Zhang, Qianyun; Malabocchia, Fabio; Bononi, Luciano
abstract

2017 - Dynamic Segment Size Selection in HTTP Based Adaptive Video Streaming [Relazione in Atti di Convegno]
Bedogni, Luca; Di Felice, Marco; Bononi, Luciano
abstract

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

2017 - Indoor Use of Gray and White Spaces: Another Look at Wireless Indoor Communication [Articolo su rivista]
Bedogni, Luca; Malabocchia, Fabio; Di Felice, Marco; Bononi, Luciano
abstract

2017 - Is WiFi suitable for energy efficient IoT deployments? A performance study [Relazione in Atti di Convegno]
Montori, Federico; Contigiani, Riccardo; Bedogni, Luca
abstract

2017 - Message from the Chairs [Relazione in Atti di Convegno]
Bedogni, L.; Cheong, S. T.; Qin, Z.
abstract

2017 - Performance assessment and feasibility analysis of IEEE 802.15.4m wireless sensor networks in TV grayspaces [Articolo su rivista]
Bedogni, Luca; Achtzehn, Andreas; Petrova, Marina; Mähönen, Petri; Bononi, Luciano
abstract

2016 - A Route Planner Service with Recharging Reservation: Electric Itinerary with a Click [Articolo su rivista]
Bedogni, Luca; Bononi, Luciano; Di Felice, Marco; D'Elia, Alfredo; Salmon Cinotti, Tullio
abstract

2016 - A self-adapting algorithm based on atmospheric pressure to localize indoor devices [Relazione in Atti di Convegno]
Bedogni, Luca; Franzoso, Fabio; Bononi, Luciano
abstract

2016 - An Integrated Simulation Framework to Model Electric Vehicle Operations and Services [Articolo su rivista]
Bedogni, L.; Bononi, L.; Di Felice, M.; D'Elia, A.; Mock, R.; Morandi, F.; Rondelli, S.; Salmon Cinotti, T.; Vergari, F.
abstract

At present, battery-charging operations constitute one of the most critical obstacles toward a large-scale uptake of electric mobility (EM), due to performance issues and implementation complexities. Although several solutions based on the utilization of information and communication technologies and on mobile applications have been investigated to assist electric vehicle (EV) drivers and to coordinate charging operations, there is still the problem of how to evaluate and validate such solutions on realistic scenarios, due to the lack of accurate simulators integrating vehicular mobility, wireless communication, and battery charging/discharging models. In this paper, we attempt to fill this gap by proposing a novel EV simulation platform that can assist in the predeployment of charging infrastructures and services on realistic large-scale EM scenarios. The simulation platform, which is realized within the ARTEMIS EU project "Internet of Energy for Electric Mobility," supports two utilization modes, i.e., evaluation of EM scenarios and immersive emulation of EM-related mobile applications, due to a semantic architecture through which virtual and real components can be integrated in a seamless way. We provide three major contributions with respect to the state of the art. First, we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation). Second, we validate the battery model against the consumptions data of target EVs, and we evaluate the operations of EVs on a large-scale scenario (the city of Bologna, Italy), by analyzing the effectiveness of the charging reservation process and the resulting impact to the smart grid. Finally, we introduce the Mobile Application Zoo, which is a sandbox through which EM-related mobile applications can be seamlessly integrated within the simulation platform to be validated on virtual environments before their deployment on real scenarios, and we describe the implementation of an Android application for battery monitoring and charging reservation.

2016 - An integrated traffic and power grid simulator enabling the assessment of e-mobility impact on the grid: a tool for the implementation of the smart grid/city concept [Relazione in Atti di Convegno]
Bedogni, Luca; Bononi, Luciano; Borghetti, Alberto; Bottura, Riccardo; D'Elia, Alfredo; Di Felice, Marco; Montori, Federico; Napolitano, Fabio; Nucci Carlo, Alberto; Salmon Cinotti, Tullio; Viola, Fabio
abstract

2016 - Context-Aware Android Applications through Transportation Mode Detection Techniques [Articolo su rivista]
Bedogni, Luca; Di Felice, Marco; Bononi, Luciano
abstract

2016 - Enhancing TV White-Spaces database with Unmanned Aerial Scanning Vehicles (UASVs) [Relazione in Atti di Convegno]
Trotta, Angelo; Bedogni, Luca; Di Felice, Marco; Bononi, Luciano; Natalizio, Enrico
abstract

2016 - Estimating urban mobility with open data: A case study in Bologna [Relazione in Atti di Convegno]
Caiati, Valeria; Bedogni, Luca; Bononi, Luciano; Ferrero, Francesco; Fiore, Marco; Vesco, Andrea
abstract

2016 - From brown coal to a rural energy landscape — Orchestration of storage and electric mobility to foster decentralized energy management [Relazione in Atti di Convegno]
D'Elia, Alfredo; Di Felice, Marco; Bedogni, Luca; Duckheim, Mathias; Mock, Randolf; Salmon Cinotti, Tullio
abstract

2016 - On the integration of heterogeneous data sources for the collaborative Internet of Things [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca; Bononi, Luciano
abstract

2016 - SenSquare: A mobile crowdsensing architecture for smart cities [Relazione in Atti di Convegno]
Montori, Federico; Bedogni, Luca; Di Chiappari, Alain; Bononi, Luciano
abstract

2016 - The Emergency Direct Mobile App: Safety Message Dissemination over a Multi-Group Network of Smartphones using Wi-Fi Direct [Relazione in Atti di Convegno]
Di Felice, Marco; Bedogni, Luca; Bononi, Luciano
abstract

Nowadays, the Wi-Fi Direct technology is supported by most of smartphones on the market, and provides a viable solution to guarantee opportunistic communication among group of devices in a 1-hop range. However, the current specifications of the standard do not support the inter-group communication, which constitutes a key requirement for content-delivery applications like the public safety ones. In this paper, we provide an in-depth analysis of the utilization of the Wi-Fi Direct technology for safety message dissemination over emergency and post-disaster scenarios. Three main contributions are provided. First, we show the experimental results of the Wi-Fi Direct technology on a test-bed composed of multiple heterogeneous smartphones, and we analyze the main factors affecting the system performance, like the network setup overhead, the communication range and the network throughput. Second, we investigate how to create multi-group Peer-to-Peer (P2P) networks by leveraging on the presence of P2P relay devices, which are in charge of offloading the data among different P2P groups, although being connected to only one P2P group at a time. An analytical model is proposed in order to derive the optimal group switching time which provides the best trade-off between the multihop delay and the delivery rate, by taking into account the buffer size of the P2P Group Owners (GO) devices. Finally, we describe the implementation of the network formation algorithm within the Emergency Direct mobile application, which allows the multi-hop dissemination of instantaneous and geo-localized alert messages among the smartphones located in the scenario of the emergency.

2016 - Workshop message: CORAL 2016 [Relazione in Atti di Convegno]
Di Felice, M.; Gao, Y. F.; Bedogni, L.; Akan, O.; Altintas, O.; Au, E.; Bansal, G.; Bayhan, S.; Caetano, M. F.; Canberk, B.; Cavalcanti, D.; De, S.; Dias, K.; Feng, Z.; Fiore, M.
abstract

2015 - Connectivity recovery in post-disaster scenarios through Cognitive Radio swarms [Articolo su rivista]
Trotta, Angelo; Di Felice, Marco; Bedogni, Luca; Bononi, Luciano; Panzieri, Fabio
abstract

2015 - Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension [Articolo su rivista]
D'Elia, Alfredo; Viola, Fabio; Montori, Federico; Di Felice, Marco; Bedogni, Luca; Bononi, Luciano; Borghetti, Alberto; Azzoni, Paolo; Bellavista, Paolo; Tarchi, Daniele; Mock, Randolf; Salmon Cinotti, Tullio
abstract

2015 - Integration of traffic and grid simulator for the analysis of e-mobility impact on power distribution networks [Relazione in Atti di Convegno]
Bedogni, L.; Bononi, L.; Borghetti, A.; Bottura, R.; D'Elia, A.; Salmon Cinotti, T.
abstract

2015 - On 3-dimensional spectrum sharing for TV white and Gray Space networks [Relazione in Atti di Convegno]
Bedogni, Luca; Trotta, Angelo; Di Felice, Marco
abstract

2015 - Park Here! a smart parking system based on smartphones' embedded sensors and short range Communication Technologies [Relazione in Atti di Convegno]
Salpietro, Rosario; Bedogni, Luca; Di Felice, Marco; Bononi, Luciano
abstract

2015 - STEM-NET: How to deploy a self-organizing network of mobile end-user devices for emergency communication [Articolo su rivista]
Aloi, G.; Bedogni, L.; Bononi, L.; Briante, O.; Di Felice, M.; Loscrì, V.; Pace, P.; Panzieri, F.; Ruggeri, G.; Trotta, A.
abstract

2015 - The Bologna ringway dataset: Improving road network conversion in SUMO and validating urban mobility via navigation services [Articolo su rivista]
Bedogni, Luca; Gramaglia, Marco; Vesco, Andrea; Fiore, Marco; Härri, Jérôme; Ferrero, Francesco
abstract

2015 - WhatIF Application: Moving Electrically without an Electric Vehicle [Relazione in Atti di Convegno]
Bedogni, Luca; Bononi, Luciano; Di Felice, Marco; D'Elia, Alfredo; Salmon Cinotti, Tullio
abstract

2015 - Workshop program (CORAL 2015) [Relazione in Atti di Convegno]
Felice, M. D.; Gao, Y. F.; Bedogni, L.
abstract

2014 - A collision-free contention protocol based on pulse/tone signals [Relazione in Atti di Convegno]
Caetano Marcos, F.; Bordim Jacir, L.; Bedogni, Luca; Bononi, Luciano
abstract

2014 - A mobile application to assist electric vehicles' drivers with charging services [Relazione in Atti di Convegno]
Bedogni, Luca; Bononi, Luciano; D'Elia, Alfredo; Di Felice, Marco; Rondelli, Simone; Salmon Cinotti, Tullio
abstract

2014 - Cognitive modulation and coding scheme adaptation for 802.11n and 802.11af networks [Relazione in Atti di Convegno]
Bedogni, Luca; Di Felice, Marco; Malabocchia, Fabio; Bononi, Luciano
abstract

2014 - Distributed mobile Femto-Databases for cognitive access to TV white spaces [Relazione in Atti di Convegno]
Bedogni, Luca; Di Felice, Marco; Trotta, Angelo; Bononi, Luciano
abstract

2014 - Driving without anxiety: A route planner service with range prediction for the electric vehicles [Relazione in Atti di Convegno]
Bedogni, Luca; Bononi, Luciano; D'Elia, Alfredo; Di Felice, Marco; Di Nicola, Marco; Salmon Cinotti, Tullio
abstract

2014 - Indoor communication over TV gray spaces based on spectrum measurements [Relazione in Atti di Convegno]
Bedogni, Luca; Di Felice, Marco; Malabocchia, Fabio; Bononi, Luciano
abstract

2014 - STEM-Net: an evolutionary network architecture for smart and sustainable cities [Articolo su rivista]
Aloi, Gianluca; Bedogni, Luca; Di Felice, Marco; Loscrì, Valeria; Molinaro, Antonella; Natalizio, Enrico; Pace, Pasquale; Ruggeri, Giuseppe; Trotta, Angelo; Roberto Zema, Nicola
abstract

The concept of smart city has emerged worldwide as a feasible answer to the challenges raised by the increasing urbanisation. From the technological point of view, guaranteeing ubiquitous connectivity, reliable communications and seamless integration of multiple network access technologies are mandatory in a smart city. This is in contrast with the current infrastructure deployment in several urban areas, which is characterised by lack of ubiquitous connectivity and coverage and by fragmentation of networks that are usually deployed by different operators and without any centralised control by the city authorities. In this paper, we look at the heterogeneity of devices and network technologies under a different perspective by not perceiving it as a limitation but as a potential to increase the connectivity in a smart city. We propose a new generation of network nodes, called stem nodes, based on the innovative idea of ‘stemness’, which pushes forward the well-known self-configuration and self-management concepts towards the idea of node mutation and evolution. We also deployed prototypes that demonstrate the stem-node architecture and basic operations in different hardware platforms of common communication devices (an Alix-based router, a laptop and a smartphone).

2014 - Self-organizing aerial mesh networks for emergency communication [Relazione in Atti di Convegno]
Di Felice, Marco; Trotta, Angelo; Bedogni, Luca; Chowdhury Kaushik, Roy; Bononi, Luciano
abstract

2014 - Smart meters with TV gray spaces connectivity: A feasibility study for two reference network topologies [Relazione in Atti di Convegno]
Bedogni, Luca; Achtzehn, Andreas; Petrova, Marina; Mahonen, Petri
abstract

2013 - An Interoperable Architecture for Mobile Smart Services over the Internet of Energy [Relazione in Atti di Convegno]
Bedogni, L.; Bononi, L.; Di Felice, M.; D'Elia, A.; Mock, R.; Montori, F.; Morandi, F.; Roffia, L.; Rondelli, S.; Salmon Cinotti, T.; Vergari, F.
abstract

2013 - Group Communication on Highways: An Evaluation Study of Geocast Protocols and Applications [Articolo su rivista]
Di Felice, M.; Bedogni, L.; Bononi, L.
abstract

2013 - Internet of Things: a process calculus approach [Relazione in Atti di Convegno]
Lanese, Ivan; Bedogni, Luca; Di Felice, Marco
abstract

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

2013 - Machine-to-Machine Communication over TV White Spaces for Smart Metering Applications [Relazione in Atti di Convegno]
Bedogni, L.; Trotta, A.; Di Felice, M.; Bononi, L.
abstract

2013 - Re-establishing Network Connectivity in Post-Disaster Scenarios Through Mobile Cognitive Radio Networks [Relazione in Atti di Convegno]
Trotta, A.; Di Felice, M.; Bedogni, L.; Bononi, L.
abstract

2013 - STEM-Mesh: Self-Organizing Mobile Cognitive Radio Network for Disaster Recovery Operations [Relazione in Atti di Convegno]
Di Felice, M.; Trotta, A.; Bedogni, L.; Bononi, L.; Panzieri, F.; Ruggeri, G.; Loscrí, V.; Pace, P.
abstract

2013 - Smartphones Like Stem Cells: Cooperation and Evolution for Emergency Communication in Post-Disaster Scenarios [Relazione in Atti di Convegno]
Di Felice, M.; Bedogni, L.; Trotta, A.; Bononi, L.; Panzieri, F.; Ruggeri, G.; Aloi, G.; Loscri, V.; Pace, P.
abstract

2012 - By Train or By Car? Detecting the User’s Motion Type through Smartphone Sensors Data [Relazione in Atti di Convegno]
Bedogni, L; Di Felice, M; Bononi, L
abstract

2012 - DySCO: A DYnamic Spectrum and COntention Control Framework for Enhanced Broadcast Communication in Vehicular Networks [Relazione in Atti di Convegno]
Di Felice, M; Bedogni, L; Bononi, L
abstract

2011 - Dynamic backbone for fast information delivery in vehicular ad hoc networks: an evaluation study [Relazione in Atti di Convegno]
Di Felice, M.; Bedogni, L.; Bononi, L.
abstract