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Nicola BICOCCHI

Professore Associato
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2023 - Let's stay close: An examination of the effects of imagined contact on behavior toward children with disability [Articolo su rivista]
Cocco, V. M.; Bisagno, E.; Bernardo, G. A. D.; Bicocchi, N.; Calderara, S.; Palazzi, A.; Cucchiara, R.; Zambonelli, F.; Cadamuro, A.; Stathi, S.; Crisp, R.; Vezzali, L.
abstract

In line with current developments in indirect intergroup contact literature, we conducted a field study using the imagined contact paradigm among high-status (Italian children) and low-status (children with foreign origins) group members (N = 122; 53 females, mean age = 7.52 years). The experiment aimed to improve attitudes and behavior toward a different low-status group, children with disability. To assess behavior, we focused on an objective measure that captures the physical distance between participants and a child with disability over the course of a five-minute interaction (i.e., while playing together). Results from a 3-week intervention revealed that in the case of high-status children imagined contact, relative to a no-intervention control condition, improved outgroup attitudes and behavior, and strengthened helping and contact intentions. These effects however did not emerge among low-status children. The results are discussed in the context of intergroup contact literature, with emphasis on the implications of imagined contact for educational settings.

2023 - Measuring Digital Twin Entanglement in Industrial Internet of Things [Relazione in Atti di Convegno]
Bellavista, Paolo; Bicocchi, Nicola; Fogli, Mattia; Giannelli, Carlo; Mamei, Marco; Picone, Marco
abstract

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this paper, we propose a concise yet expressive metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness. Furthermore, the paper presents the development of our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

2023 - Proxemic Behaviors During Gay/Straight Interactions: An Automated Analysis Through Kinect Depth-Sensing Camera [Articolo su rivista]
Sacino, Alessandra; Bicocchi, Nicola; Di Bernardo, Gian Antonio; Pecini, Chiara; Di Gesto, Cristian; Maragliano, Andrea; Vezzali, Loris; Andrighetto, Luca
abstract

Through two experimental studies (N = 150), we investigated proxemic behaviors featuring gay/straight dyadic interactions. In doing so, for the first time, we relied on an IR depth camera and considered the interpersonal volume between the interactants, a novel feature that exhaustively captures interactants' proxemic behaviors. Study 1 revealed that the straight participants' implicit sexual bias - but not the explicit prejudice - significantly predicted their volume while interacting with a study accomplice who was presented as gay (vs. straight). However, unlike previous research, mixed-model analyses revealed the higher their implicit bias was, the smaller the interpersonal volume that they maintained with the gay study accomplice, especially when the conversation focused on an intergroup-related (vs. neutral) topic. Study 2 was mainly designed to deepen this main finding. Results documented that highly implicitly biased participants who maintained a smaller interpersonal volume with a gay (vs. straight) study accomplice were more cognitively depleted after the interaction than low-biased participants, possibly suggesting that highly implicitly biased straight people can control this nonverbal behavior to appear as nonprejudiced in the gay interactant's eyes. Implications for research on sexual prejudice and intergroup nonverbal behaviors are discussed.

2023 - Requirements and design patterns for adaptive, autonomous, and context-aware digital twins in industry 4.0 digital factories [Articolo su rivista]
Bellavista, Paolo; Bicocchi, Nicola; Fogli, Mattia; Giannelli, Carlo; Mamei, Marco; Picone, Marco
abstract

2022 - IoT-PROD 2022: First International Workshop on Internet of Things Pervasive Real-World Deployments - Welcome and Committees: Welcome Message [Relazione in Atti di Convegno]
Artemenko, A.; Bicocchi, N.; Picone, M.; Weis, T.; Zdankin, P.
abstract

2021 - Forecasting Parking Lots Availability: Analysis from a Real-World Deployment [Relazione in Atti di Convegno]
Barraco, M.; Bicocchi, N.; Mamei, M.; Zambonelli, F.
abstract

Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis.

2020 - Smart cities in the fog: Clearing the vision of innovative sensing applications [Capitolo/Saggio]
Bicocchi, N.; Canali, C.; Lancellotti, R.
abstract

The new paradigm of smart cities is deeply intertwined with the development of large-scale sensing applications. An ever-growing amount of sensors are collecting data to support decision strategies for the management of the city services. Examples of such applications are traffic monitoring, autonomous driving, environmental sensing, real-time power/resource utilization metering. A traditional cloud-based approach for the deployment of such services is likely to suffer from performance and QoS problems due to the risk of congestion on the data center outbound links and due to high latency related to the geographic data exchange. An alternative paradigm to mitigate these problems is the fog computing, where a layer of intermediate fog nodes is placed between the sensors and the cloud data center to reduce the amount of data exchanges (through aggregation and filtering) and to host latency-critical services. The fog computing opens several new issues for the management and deployment of the services, especially if we consider that new applications may be dynamically deployed and also the infrastructure is subject to changes over time (e.g., adding and removing sensors and fog nodes). While this dynamic behavior can be supported by existing technologies such as containers, service orchestration frameworks, and micro-services, the fog paradigm exacerbates the problem of infrastructure and service coordination and management to the point where new solutions must be devised. The critical challenges that should be addressed by future fog infrastructures for smart cities lie in the area of service management, optimization of the infrastructure and automatic deployment of applications. In the present chapter, we discuss advantages and disadvantages of solutions for the management of smart city sensing applications, considering architectures, optimization models, algorithms for the service deployment, and the support for the applications life cycle.

2020 - The SOTA approach to engineering collective adaptive systems [Articolo su rivista]
Abeywickrama, Dhaminda B.; Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

The emergence of collective adaptive systems-i.e., computational systems made up of an ensemble of autonomous components that have to operate in a coordinated and adaptive way in open-ended and unpredictable environments-calls for innovative modeling and software engineering tools, to support their systematic and rigorous design and development. In this paper, we present a general model for collective adaptive systems called SOTA ("State Of The Affairs"). SOTA brings together the lessons of goal-oriented requirements modeling, context-aware system modeling, and dynamical systems modeling. It has the potential for acting as a general reference model to help tackling some key issues in the design and development of collective adaptive systems. In particular, as we will show with reference to a scenario of collectives of autonomous vehicles, SOTA enables: early verification of requirements, identification of knowledge requirements for self-adaptation, and the identification of the most suitable architectural patterns for self-adaptation.

2019 - A Survey of the Use of Software Agents in Digital Factories [Relazione in Atti di Convegno]
Bicocchi, N.; Cabri, G.; Leonardi, L.; Salierno, G.
abstract

Digital factories represent an abstraction of real factories, which is useful to manage at a high level the processes as well as the interactions inside the factories but also the interactions between factories. This abstraction can automatize several processes and can enable to dynamically adapt the factory production to unexpected situations. Software agents can meet the requirements of digital factories by means of their features of autonomy, reactivity, proactivity and sociality. In this paper, we survey the use of software agents in the context of digital factories, showing how they can be exploited. A discussion about the advantages brought by software agents and the limitation of agent-based approaches completes the paper.

2019 - A Technique to Identify Data Exchange Between Cloud Virtual Machines [Capitolo/Saggio]
Bicocchi, N.; Canali, C.; Lancellotti, R.
abstract

Modern cloud data centers typically exploit management strategies to reduce the overall energy consumption. While most of the solutions focus on the energy consumption due to computational elements, the optimization of network-related aspects of a data center is becoming more and more important, considering also the advent of the Software-Defined Network paradigm. However, an enabling step to implement network-aware Virtual Machine (VM) allocation is the knowledge of data exchange patterns. In this way we can place in well-connected hosts (or on the same physical host) the couples of VMs that exchange a large amount of information. Unfortunately, in Infrastructure as a Service data centers, a detailed knowledge on VMs data exchange is seldom available without the deployment of a specialized (and costly) monitoring infrastructure. In this paper, we propose a technique to infer VMs communication patterns starting from input/output network traffic time series of each VM. We discuss both the theoretical aspect of such technique and the design challenges for its implementation. A case study is used to demonstrate the viability of our idea.

2019 - An architectural approach for digital factories [Relazione in Atti di Convegno]
Bicocchi, N.; Cabri, G.; Leotta, F.; Mandreoli, F.; Mecella, M.; Sapio, F.
abstract

Digital factories comprise a multi-layered integration of various activities along the factories and product life-cycles. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory expands outside the company boundaries and allows to collaborate on business processes over the whole supply chain. This extended abstract, based on a recently published paper, discusses an interoperability architecture for digital factories. It analyses the key requirements for enabling a scalable factory architecture characterized by access to services, aggregation of data, and orchestration of production processes.

2019 - CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities [Articolo su rivista]
Bicocchi, N.; Fontana, D.; Zambonelli, F.
abstract

Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms.

2019 - Dynamic digital factories for agile supply chains: An architectural approach [Articolo su rivista]
Bicocchi, Nicola; Cabri, Giacomo; Mandreoli, Federica; Mecella, Massimo
abstract

Digital factories comprise a multi-layered integration of various activities along the factories and product lifecycles. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory thus expands outside the company boundaries and offers the opportunity to collaborate on business processes affecting the whole supply chain. This paper discusses an interoperability architecture for digital factories. To this end, it delves into the issue by analysing the key requirements for enabling a scalable factory architecture characterized by access to services, aggregation of data, and orchestration of production processes. Then, the paper revises the state-of-the-art w.r.t. these requirements and proposes an architectural framework conjugating features of both service-oriented and data-sharing architectures. The framework is exemplified through a case study.

2019 - Evaluating origin–destination matrices obtained from CDR data [Articolo su rivista]
Mamei, M.; Bicocchi, N.; Lippi, M.; Mariani, S.; Zambonelli, F.
abstract

Understanding and correctly modeling urban mobility is a crucial issue for the development of smart cities. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) can naturally support urban and transport studies as well as marketing applications. Individual trips are often aggregated in an origin–destination (OD) matrix counting the number of trips from a given origin to a given destination. In the literature dealing with CDR data there are two main approaches to extract OD matrices from such data: (a) in time-based matrices, the analysis focuses on estimating mobility directly from a sequence of CDRs; (b) in routine-based matrices (OD by purpose) the analysis focuses on routine kind of movements, like home-work commute, derived from a trip generation model. In both cases, the OD matrix measured by CDR counts is scaled to match the actual number of people moving in the area, and projected to the road network to estimate actual flows on the streets. In this paper, we describe prototypical approaches to estimate OD matrices, describe an actual implementation, and present a number of experiments to evaluate the results from multiple perspectives.

2019 - Gait-Based Diplegia Classification Using LSMT Networks [Articolo su rivista]
Ferrari, Alberto; Bergamini, Luca; Guerzoni, Giorgio; Calderara, Simone; Bicocchi, Nicola; Vitetta, Giorgio; Borghi, Corrado; Neviani, Rita; Ferrari, Adriano
abstract

Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diplegic patients into 4 main forms, each one associated with a peculiar walking pattern. In this work, we apply two different deep learning techniques, namely, multilayer perceptron and recurrent neural networks, to automatically classify children into the 4 clinical forms. For the analysis, we used a dataset comprising gait data of 174 patients collected by means of an optoelectronic system. The measurements describing walking patterns have been processed to extract 27 angular parameters and then used to train both kinds of neural networks. Classification results are comparable with those provided by experts in 3 out of 4 forms.

2019 - Intelligent agents supporting digital factories [Relazione in Atti di Convegno]
Bicocchi, N.; Cabri, G.; Leonardi, L.; Salierno, G.
abstract

Intelligent agents represent a widely exploited paradigm of the Distributed Artificial Intelligence (DAI). They have been applied in many fields, and recently they have appeared also in the digital factory field. Digital factories are abstractions of real factories, which enable high-level management of factories' processes, along with their automatization. So, the real factories can dynamically adapt their processes to unexpected situations. In this paper, we survey different works at the state of the art that show how intelligent agents can support digital factories, along with the limitations of their application. A discussion about the advantages of intelligent agents and the open issues completes the paper.

2019 - User-aware comfort in retail environments [Relazione in Atti di Convegno]
Bicocchi, N.; Boese, S.; Cabri, G.
abstract

A retail environment can be thought as an environment where customers can buy products, goods or services. The user-experience in physical retail environments is important not only for facilitating the selling of goods and services, but also for providing satisfaction and appealing to retain customers over the long term. The user-experience can be enhanced by adapting aspects of the physical environment such as music, colour, fragrance to the tastes of the customers. In this paper we propose a user-aware approach to adapt physical aspects of a retail environment in order to improve the perceived comfort level. We introduce a model of the user context, which can be used both for representing information about the customers and for driving the adaptation of the environment. The proposed decision system is based on a microservice architecture providing both modularity and flexibility. Real-world examples are also used to show applications of the approach.

2018 - Comportamento non verbale intergruppi “oggettivo”: una replica dello studio di Dovidio, kawakami e Gaertner (2002) [Abstract in Atti di Convegno]
Di Bernardo, Gian Antonio; Vezzali, Loris; Giovannini, Dino; Palazzi, Andrea; Calderara, Simone; Bicocchi, Nicola; Zambonelli, Franco; Cucchiara, Rita; Cadamuro, Alessia; Cocco, Veronica Margherita
abstract

Vi è una lunga tradizione di ricerca che ha analizzato il comportamento non verbale, anche considerando relazioni intergruppi. Solitamente, questi studi si avvalgono di valutazioni di coder esterni, che tuttavia sono soggettive e aperte a distorsioni. Abbiamo condotto uno studio in cui si è preso come riferimento il celebre studio di Dovidio, Kawakami e Gaertner (2002), apportando tuttavia alcune modifiche e considerando la relazione tra bianchi e neri. Partecipanti bianchi, dopo aver completato misure di pregiudizio esplicito e implicito, incontravano (in ordine contro-bilanciato) un collaboratore bianco e uno nero. Con ognuno di essi, parlavano per tre minuti di un argomento neutro e di un argomento saliente per la distinzione di gruppo (in ordine contro-bilanciato). Tali interazioni erano registrate con una telecamera kinect, che è in grado di tenere conto della componente tridimensionale del movimento. I risultati hanno rivelato vari elementi di interesse. Anzitutto, si sono creati indici oggettivi, a partire da un’analisi della letteratura, alcuni dei quali non possono essere rilevati da coder esterni, quali distanza interpersonale e volume di spazio tra le persone. I risultati hanno messo in luce alcuni aspetti rilevanti: (1) l’atteggiamento implicito è associato a vari indici di comportamento non verbale, i quali mediano sulle valutazioni dei partecipanti fornite dai collaboratori; (2) le interazioni vanno considerate in maniera dinamica, tenendo conto che si sviluppano nel tempo; (3) ciò che può essere importante è il comportamento non verbale globale, piuttosto che alcuni indici specifici pre-determinati dagli sperimentatori.

2018 - Dealing with data and software interoperability issues in digital factories [Relazione in Atti di Convegno]
Bicocchi, Nicola; Cabri, Giacomo; Mandreoli, Federica; Mecella, Massimo
abstract

The digital factory paradigm comprises a multi-layered integration of the information related to various activities along the factory and product lifecycle manufacturing related resources. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory thus expands outside the actual company boundaries and offers the opportunity for the business and its suppliers to collaborate on business processes that affect the whole supply chain. This paper discusses an interoperability architecture for digital factories. To this end, it delves into the issue by analysing the main challenges that must be addressed to support an integrated and scalable factory architecture characterized by access to services, aggregation of data, and orchestration of production processes. Then, it revises the state of the art in the light of these requirements and proposes a general architectural framework conjugating the most interesting features of serviceoriented architectures and data sharing architectures. The study is exemplified through a case study.

2018 - Using Kinect camera for investigating intergroup non-verbal human interactions [Abstract in Atti di Convegno]
Vezzali, Loris; Di Bernardo, Gian Antonio; Cadamuro, Alessia; Cocco, Veronica Margherita; Crapolicchio, Eleonora; Bicocchi, Nicola; Calderara, Simone; Giovannini, Dino; Zambonelli, Franco; Cucchiara, Rita
abstract

A long tradition in social psychology focused on nonverbal behaviour displayed during dyadic interactions generally relying on evaluations from external coders. However, in addition to the fact that external coders may be biased, they may not capture certain type of behavioural indices. We designed three studies examining explicit and implicit prejudice as predictors of nonberval behaviour as reflected in objective indices provided by Kinect cameras. In the first study, we considered White-Black relations from the perspective of 36 White participants. Results revealed that implicit prejudice was associated with a reduction in interpersonal distance and in the volume of space between Whites and Blacks (vs. Whites and Whites), which in turn were associated with evaluations by collaborators taking part in the interaction. In the second study, 37 non-HIV participants interacted with HIV individuals. We found that implicit prejudice was associated with reduced volume of space between interactants over time (a process of bias overcorrection) only when they tried hard to control their behaviour (as captured by a stroop test). In the third study 35 non-disabled children interacted with disabled children. Results revealed that implicit prejudice was associated with reduced interpersonal distance over time.

2017 - A new era in the study of intergroup nonverbal behaviour: Studying intergroup dyadic interactions “online” [Abstract in Atti di Convegno]
DI BERNARDO, GIAN ANTONIO; Vezzali, Loris; Palazzi, Andrea; Calderara, Simone; Bicocchi, Nicola; Zambonelli, Franco; Cucchiara, Rita; Cadamuro, Alessia
abstract

We examined predictors and consequences of intergroup nonverbal behaviour by relying on new technologies and new objective indices. In three studies, both in the laboratory and in the field with children, behaviour was a function of implicit prejudice.

2017 - On Recommending Opportunistic Rides [Articolo su rivista]
Bicocchi, Nicola; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Research on social and mobile technologies recently provided tools to collect and mine massive amounts of mobility data. Ride sharing is one of the most prominent applications in this area. While a number of research and commercial initiatives already proposed solutions for long-distance journeys, the opportunities provided by modern pervasive systems can be used to promote local, daily ride sharing within the city. We present a set of algorithms to analyze urban mobility traces and to recognize matching rides along similar routes. These rides are amenable for ride sharing recommendations. We validate the proposed methodology using data provided by a large Italian telecom operator. Assuming the full set of considered users are willing to accept 1-km detours, experimental results on two large cities show that more than 60% of trips could be saved. These results can be used to evaluate the potential of a ride sharing system before its actual deployment and to actually support an opportunistic ride sharing recommender system.

2017 - Signal Processing and Machine Learning for Diplegia Classification [Relazione in Atti di Convegno]
Bergamini, Luca; Calderara, Simone; Bicocchi, Nicola; Ferrari, Alberto; Vitetta, Giorgio
abstract

Diplegia is one of the most common forms of a broad family of motion disorders named cerebral palsy (CP) affecting the voluntary muscular system. In recent years, various classification criteria have been proposed for CP, to assist in diagnosis, clinical decision-making and communication. In this manuscript, we divide the spastic forms of CP into 4 other categories according to a previous classification criterion and propose a machine learning approach for automatically classifying patients. Training and validation of our approach are based on data about 200 patients acquired using 19 markers and high frequency VICON cameras in an Italian hospital. Our approach makes use of the latest deep learning techniques. More specifically, it involves a multi-layer perceptron network (MLP), combined with Fourier analysis. An encouraging classification performance is obtained for two of the four classes.

2016 - Spotting prejudice with nonverbal behaviours [Relazione in Atti di Convegno]
Palazzi, Andrea; Calderara, Simone; Bicocchi, Nicola; Vezzali, Loris; DI BERNARDO, GIAN ANTONIO; Zambonelli, Franco; Cucchiara, Rita
abstract

Despite prejudice cannot be directly observed, nonverbal behaviours provide profound hints on people inclinations. In this paper, we use recent sensing technologies and machine learning techniques to automatically infer the results of psychological questionnaires frequently used to assess implicit prejudice. In particular, we recorded 32 students discussing with both white and black collaborators. Then, we identified a set of features allowing automatic extraction and measured their degree of correlation with psychological scores. Results confirmed that automated analysis of nonverbal behaviour is actually possible thus paving the way for innovative clinical tools and eventually more secure societies.

2015 - Opportunistic Ride Sharing via Whereabouts Analysis [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. We present a methodology, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. Results on a real dataset show that, assuming users are willing to share rides and tolerate 1Km detours, about 60% of trips could be saved.

2015 - Reasoning on data streams: An approach to adaptation in pervasive systems [Relazione in Atti di Convegno]
Bicocchi, N.; Vassev, E.; Zambonelli, F.; Hinchey, M.
abstract

Urban environments are increasingly invaded by devices that acquire sensor information and pave the way for innovative forms of context awareness. Collecting knowledge from loosely-structured data streams and reasoning about changes are two key elements of the process. This paper illustrates a possible way to combine these two elements in a coordinated way. We make use of a recently-developed framework for classifying data streams with service-oriented, reconfigurable components. Furthermore, we embed the KnowLang Reasoner, allowing logical and statistical reasoning on the acquired knowledge aiming to achieve self-adaptation.

2015 - Software-Intensive Systems for Smart Cities: from Ensembles to Superorganisms [Capitolo/Saggio]
Bicocchi, Nicola; Leonardi, Letizia; Zambonelli, Franco
abstract

Smart cities infrastructures can be considered as large-scale, software-intensive systems exhibiting close sinergies among ICT devices and humans. However, current deployments of smart city technologies rely on rather traditional technologies. This chapter introduces a novel perspective in which large-scale ensembles of software components, ICT devices, and humans, can be made working together in an orchestrated and self-organized way to achieve urban-level goals as if they were part of a single large-scale organism, i.e., a superorganism. Accordingly, we delineate our vision of urban superorganisms and overview related application areas. Finally, we identify the key challenges in engineering selforganizing systems that can work as a superorganism, and we introduce the reference architecture for an infrastructure capable of supporting our vision.

2014 - A Self-Reconfigurable Framework for Context Awareness [Relazione in Atti di Convegno]
Bicocchi, Nicola; Fontana, Damiano; Zambonelli, Franco
abstract

Urban environments are increasingly pervaded by ICT devices. Soon, citizens and technologies could collaboratively constitute large-scale socio-technical organisms supporting both individual and collective awareness. This paper illustrates a modern awareness framework designed to deal with the complexity of this scenario. The framework is able to collect and classify data streams in a modular way by supporting service oriented, reconfigurable components. Furthermore, we evaluate an innovative meta-classifcation scheme based on state-automata for (i) improving energy efficiency, (ii) improving classification accuracy and (iii) improving software engineering of aware systems, without affecting the overall performance.

2014 - Human aware superorganisms [Relazione in Atti di Convegno]
Bicocchi, Nicola; Fontana, Damiano; Zambonelli, Franco
abstract

Massive networks of wearable devices have recently become a key scenario for pattern recognition technologies. Applications range from implicit human-machine interactions, to autonomous monitoring of user habits and activities. This paper presents a framework providing developers with tools to orchestrate the continuous process of collecting and classifying data streams in aware-systems. It supports service oriented, reconfigurable components and provides a solid background to put at joint work specification- and data-driven approaches. It also provides an innovative meta-classification scheme allowing to implement applications by editing a simple state automata. Experimental results suggest that the approach could be integrated in a number of applications for: (i) improving energy efficiency, (ii) improving classification accuracy and (iii) improving software engineering of aware systems.

2014 - Improving Activity Recognition via Satellite Imagery and Commonsense Knowledge [Relazione in Atti di Convegno]
Bicocchi, Nicola; Fontana, Damiano; Zambonelli, Franco
abstract

Activity recognition gained relevance in recent years because of its numerous applications. Despite relevant improvements, current classifiers are still inaccurate in several usage conditions or require time-consuming training. In this paper we show how localisation data and common sense knowledge could be used to improve activity recognition. More specifically, given the GPS position of the user, we both gather (i) a list of neighbouring commercial activities using a reverse geo-coding service and (ii) classify the satellite image of the area with state-of-the-art techniques. The approach maps classification labels produced by the three classifiers (i.e., activity, reverse geocoding localisation, satellite imagery localisation) to concepts within the ConceptNet network for the sake of improving activity recognition accuracy.

2014 - Investigating Ride Sharing Opportunities through Mobility Data Analysis [Articolo su rivista]
Bicocchi, Nicola; Mamei, Marco
abstract

Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. Our goal is to develop a recommender system - to be integrated in smart phones, tablets, and in-vehicle platforms - capable of identifying opportunities for sharing cars and rides. We present a methodol- ogy, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. We provide experimental results showing the impact of this technology and we illustrate aWeb-based platform implementing the key concepts presented.

2014 - Re-identification of Anonymized CDR datasets Using Social Network Data [Relazione in Atti di Convegno]
Cecaj, Alket; Mamei, Marco; Bicocchi, Nicola
abstract

In this work we examine a large dataset of 335 million anonymized call records made by 3 million users during 47 days in a region of northern Italy. Combining this dataset with publicly available user data, from different social networking ser-vices, we present a probabilistic approach to evaluate the potential of re-identification of the anonymized call records dataset. In this sense, our work explores different ways of analyzing data and data fusion techniques to integrate different mobility datasets together. On the one hand, this kind of approaches can breach users' privacy despite anonymization, so it is worth studying carefully. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information form multiple sources can complement and enrich each other.

2014 - Social Collective Awareness in Socio-Technical Urban Superorganisms [Capitolo/Saggio]
Bicocchi, Nicola; Cecaj, Alket; Fontana, Damiano; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Smart cities are characterized by the close integration of ICT devices and humans. However, the vast majority of current deployments of smart technologies relies on sensing devices collecting data and data mining techniques squeezing little meanings out of them. Nevertheless, we believe that citizens integrated with ICT technologies could collaboratively constitute large-scale socio-technical superorganisms supporting collective awareness and behaviours. This paper clarifies our vision on urban superorganisms, identifies the key challenges towards their actual deployment and proposes a prototype architecture supporting their development.

2014 - Towards a human-aware operating system [Relazione in Atti di Convegno]
Bicocchi, N.; Fontana, D.; Zambonelli, F.
abstract

Body-area networks have become a key scenario for pattern recognition technologies. Applications range from implicit human-machine interactions, to autonomous monitoring of user habits and activities. This paper presents a general framework that provide developers with tools to orchestrate the continuous process of collecting and classifying data streams. This can facilitate the development of humanaware applications, i.e., applications that can adapt to the context of their users. The framework supports service oriented, reconfigurable components and provides a solid background to put at joint work specification-and data-driven approaches. It also provides an innovative meta-classification scheme allowing developers to implement applications by editing a state automata. Experimental results suggest that the approach could be integrated in a number of applications for: (i) improving energy efficiency, (ii) improving classification accuracy and (iii) improving software engineering of aware systems.

2013 - Collective Awareness and Action in Urban Superorganisms [Relazione in Atti di Convegno]
Bicocchi, Nicola; Fontana, Damiano; Mamei, Marco; Zambonelli, Franco
abstract

Future urban scenarios will be characterized by the close integration of ITC devices and humans. Citizens using their own capabilities integrated with ITC technologies could collaboratively constitute a large-scale socio-technical superorganism to support collective “urban” awareness and activities. This position paper, with the help of a representative case study, identifies the key challenges for future urban superorganisms and proposes a two-tier architecture to support their development.

2013 - Collective Awareness for Human-ICT Collaboration in Smart Cities [Relazione in Atti di Convegno]
Bicocchi, Nicola; Cecaj, Alket; Fontana, Damiano; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Future urban scenarios will be characterized by the close integration of ICT devices and humans. Citizens using their own capabilities integrated with ICT technologies could collaboratively constitute a large-scale socio-technical superorganism to support collective urban awareness and activities. This position paper, with the help of a representative case study in the area of intelligent transportation systems, identifies the key challenges for future urban superorganisms and proposes a two-tier architecture to support their development.

2012 - Bridging vision and commonsense for multimodal situation recognition in pervasive systems [Relazione in Atti di Convegno]
Bicocchi, Nicola; Lasagni, Matteo; Zambonelli, Franco
abstract

Pervasive services may have to rely on multimodal classification to implement situation-recognition. However, the effectiveness of current multimodal classifiers is often not satisfactory. In this paper, we describe a novel approach to multimodal classification based on integrating a vision sensor with a commonsense knowledge base. Specifically, our approach is based on extracting the individual objects perceived by a camera and classifying them individually with non-parametric algorithms; then, using a commonsense knowledge base, classifying the overall scene with high effectiveness. Such classification results can then be fused together with other sensors, again on a commonsense basis, for both improving classification accuracy and dealing with missing labels. Experimental results are presented to assess, under different configurations, the effectiveness of our vision sensor and its integration with other kinds of sensors, proving that the approach is effective and able to correctly recognize a number of situations in open-ended environments.

2012 - Experiences on sensor fusion with commonsense reasoning [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; M., Lasagni; Mamei, Marco; Zambonelli, Franco
abstract

Multi-modal sensor fusion recently became a widespread technique to provide pervasive services with context-recognition capabilities. However, classifiers commonly used to implement this technique are still far from being perfect. Thus, fusion algorithms able to deal with significant inaccuracies are required. In this paper we present preliminary results obtained with a novel approach that combines diverse classifiers through commonsense reasoning. The approach maps classification labels produced by classifiers to concepts organized within the ConceptNet network. Then it verifies their semantic proximity by implementing a greedy sub-graph search algorithm. Specifically, different classifiers are fused together on a commonsense basis for both: (i) improving classification accuracy and (ii) dealing with missing labels. Experimental results are discussed through a real-world case study in which three classifiers are fused to recognize both user activities and locations.

2012 - SOTA: Towards a General Model for Self-Adaptive Systems [Relazione in Atti di Convegno]
Abeywickrama, Dhaminda; Bicocchi, Nicola; Zambonelli, Franco
abstract

The increasing complexity and dynamics in which software systems are deployed call for solutions to make such systems autonomic, i.e., capable of dynamically self-adapting their behavior in response to changing situations. To this end, proper models and software engineering tools are required to be available to support the design and development of autonomic systems. In this paper, we introduce a new general model, SOTA, for modeling the adaptation requirements. SOTA, by bringing together the lessons of goal-oriented modeling and of context-aware system modeling, has the potentials for tackling some key issues in the design and development of complex self-adaptive software systems. In particular, SOTA enables: early verification of requirements, identification of knowledge requirements for self-adaptation, and identification of the most suitable self-adaptive patterns.

2012 - Self-organizing Virtual Macro Sensors [Articolo su rivista]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

The future mass deployment of pervasive and dense sensor network infrastructures calls for proper mechanisms to enable extracting general-purpose data from them at limited energy costs and in a compact way. The approach presented in this paper relieson a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence of spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis.The result of this process is that a sensor network can be modeled as made up of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data related to its region and can act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and analyzed, evaluated both in a simulation environment andon a real test bed, and quantitatively compared with related works in the area. The current limitations of our approach and the areas for future research are also discussed.

2011 - Augmenting mobile localization with activities and common sense knowledge [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Zambonelli, Franco
abstract

Location is a key element for ambient intelligence services. Due to GPS inaccuracies, inferring high level information (i.e., being at home, at work, in a restaurant) from geographic coordinates in still non trivial. In this paper we use information about activities being performed by the user to improve location recognition accuracy. Unlike traditional methods, relations between locations and activities are not extracted from training data but from an external commonsense knowledge base. Our approach maps location and activity labels to concepts organized within the ConceptNet network. Then, it verifies their commonsense proximity by implementing a bio-inspired greedy algorithm. Experimental results show a sharp increase in localization accuracy.

2011 - Improving Situation Recognition via Commonsense Sensor Fusion [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Zambonelli, Franco
abstract

Pervasive services often rely on multi-modal classification to implement situation-recognition capabilities. However, current classifiers are still inaccurate and unreliable. In this paper we present preliminary results obtained with a novel approach that combines well established classifiers using a commonsense knowledge base. The approach maps classification labels produced by independent classifiers to concepts organized within the Concept Net network. Then it verifies their semantic proximity by implementing a greedy approximate sub-graph search algorithm. Specifically, different classifiers are fused together on a commonsense basis for both: (i) improve classification accuracy and (ii) deal with missing labels. Experimental results are discussed through a real-world case study in which two classifiers are fused to recognize both user's activities and visited locations.

2011 - Landslide Monitoring with Sensor Networks: Experiences and Lessons Learnt from a Real-world Deployment [Articolo su rivista]
Rosi, Alberto; M., Berti; Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Corsini, Alessandro; Zambonelli, Franco
abstract

Wireless sensor networks have the potentials to be a very useful technology for fine-grained monitoring in remote and hostile environments. This paper reports on the implementation and deployment of a system for landslide monitoring in the Northern Italy Apennines, and analyzes the positive results we have achieved with it. Yet, the paper also critically analyzes the problems and the inherent limitations/difficulties we had to face in developing and deploying such a system, challenging many of the “big claims” that are often heard around wireless sensor networks.

2011 - On Self-adaptation, Self-expression, and Self-awareness in Autonomic Service Component Ensembles [Relazione in Atti di Convegno]
Zambonelli, Franco; Bicocchi, Nicola; Cabri, Giacomo; Leonardi, Letizia; Puviani, Mariachiara
abstract

Software systems operating in open-ended andunpredictable environments have to become autonomic, i.e.,capable of dynamically adapting their behavior in response tochanging situations. To this end, key research issues include:(i) framing the schemes that can facilitate components (orensembles of) to exhibit self-adaptive behaviors; (ii) identifyingmechanisms to enable components or ensembles to self-expressthe most suitable adaptation scheme; and (iii) acquiring theproper degree of self-awareness to enable putting in action selfadaptation and self-expression schemes. In this position paper, with the help of a representative case study, we frame anddiscuss the above issues, survey the state of the art in the area,and sketch the main research challenges that will be faced inthe ASCENS project towards the definition of a fully-fledgedframework for autonomic services.

2010 - A simulation modelling approach enabling joint emergency response operations [Relazione in Atti di Convegno]
Bicocchi, N.; Ross, W.; Ulieru, M.
abstract

A novel capability for modelling and simulating intra- and inter-organizational collaboration in an emergency-response domain is presented. This capability combines the prescriptive, top-down view of organizations, which describes how they work "on paper," and the descriptive, bottom-up view, which describes how they actually work, by focusing on three components in the light of agent-based modelling and simulation tools - structural, functional, and normative. Our approach enables decision-makers to anticipate the evolution of an emerging crisis and evaluate the effectiveness of different configurations on the response. The initial results of our simulation, based on an experiment which investigates the impact of three separate parameters, are also presented and reveal the joint effectiveness of the organizations involved. ©2010 IEEE.

2010 - Detecting Activities from Body-Worn Accelerometers via Instance-based Algorithms [Articolo su rivista]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

The automatic and unobtrusive identification of user’s activities is one of the challenging goals of context-aware computing. This paper discusses and experimentally evaluates instance-based algorithms to infer user’s activities on the basis of data acquired from body-worn accelerometer sensors. We show that instance-based algorithms can classify simple and specific activities with high accuracy. In addition, due to their low requirements, we show how they can be implemented on severely resource-constrained devices. Finally, we propose mechanisms to take advantage of the temporal dimension of the signal, and to identify novel activities at run time.

2010 - Environmental Monitoring and Task-Driven Computing [Articolo su rivista]
Rosi, Alberto; M., Berti; Bicocchi, Nicola; Castelli, Gabriella; Corsini, Alessandro; Mamei, Marco; Zambonelli, Franco; e. t. a., L.
abstract

We report on our early experience in landslide monitoring with sensor networks.

2010 - Handling dynamics in diffusive aggregation schemes: An evaporative approach [Articolo su rivista]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

Distributed computing in large-size dynamic networks often requires the availability at each and every node of globally aggregated information about some overall properties of the network. In this context, traditional broadcasting solutions become inadequate as the number of participating nodes increases. Therefore, aggregation schemes inspired by the physical/biological phenomenon of diffusion have been recently proposed as a simple yet effective alternative to solve the problem. However, diffusive aggregation algorithms require solutions to cope with the dynamics of the network and/or of the values being aggregated solutions, which are typically based on periodic restarts (epoch-based approaches). This paper proposes an original and autonomic solution, relying on coupling diffusive aggregation schemes with the “bio-inspired” mechanism of evaporation. While a gossip-based diffusive communication scheme is used to aggregate values over a network, gradual evaporation of values can be exploited to account for network and value dynamics without requiring periodic restarts. A comparative performance evaluation shows that the evaporative approach is able to manage the dynamism of the values and of the network structure in an effective way: in most situations it leads to more accurate aggregate estimations than epoch-based techniques.

2010 - Self-organized Data Ecologies for Pervasive Situation-Aware Services: the Knowledge Networks Approach [Articolo su rivista]
Bicocchi, Nicola; M., Baumgarten; M., Brgulja; R., Kusber; Mamei, Marco; M., Mulvenna; Zambonelli, Franco
abstract

Pervasive computing services exploit information about the physical world both to adapt their own behavior in a context-aware way and to deliver to users enhanced means of interaction with their surrounding environment. The technology to acquire digital information about the physical world is increasingly available, making services at risk of being overwhelmed by such growing amounts of data. This calls for novel approaches to represent and automatically organize, aggregate, and prune such growing amounts of data before delivering it to services. In particular, individual data items should form a sort of self-organized ecology in which, by linking and combining with each other into sorts of “knowledge networks”, they can be able to provide to services compact and easy to be managed higher-level knowledge about situations occurring in the environment. In this context, the contribution of this paper is twofold. First, with the help of a simple case study, we motivate the need to evolve from models of “context-awareness” towards models of “situation-awareness” via proper self-organized “knowledge networks” tools, and introduce a general reference architecture for knowledge networks. Second, we describe the design and implementation of a knowledge network toolkit we have developed, and exemplify algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed.

2010 - Unsupervised Learning in Body-area Networks [Relazione in Atti di Convegno]
Bicocchi, Nicola; Lasagni, Matteo; Mamei, Marco; Prati, Andrea; Cucchiara, Rita; Zambonelli, Franco
abstract

Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.

2009 - An Evaporative Approach to Handle Dynamics in Diffusive Aggregation Schemes [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

Distributed computing in large-size dynamic networks oftenrequires the availability at each and every node globally ag-gregated information about some overall properties of thenetwork. In this context, since traditional broadcasting so-lutions become inadequate as the number of participatingnodes increases, aggregation schemes inspired by the phys-ical/biological phenomenon of diusion have been recentlyproposed as a simple yet eective alternative to solve theproblem. However, diusive aggregation requires specicsolutions to cope with the dynamics of the network and/orof the values being aggregated. While typical solutions arebased on periodic restarts (epoch-based approaches), in thispaper, we propose an original and more autonomic solution,relying on coupling diusive aggregation schemes with theadditional bio-inspired mechanism of evaporation. While agossip-based diusive communication scheme is used to ag-gregate values over a network, gradual evaporation of valuescan be exploited to account for network and value dynamicswithout requiring periodic restarts. A comparative perfor-mance evaluation shows that the evaporative approach isable to manage the dynamism of the values sensed over thenetwork in an eective way and, in the most of the cases,it leads to more accurate aggregate estimations than epoch-based techniques.

2009 - Handling Dynamics in Gossip-based Aggregation Schemes [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

A problem in large and dynamic networks consists in making available at each node global information about the state of the network. Gossip-based aggregation schemes are a simple yet effective mechanism to solve the problem. However, they have to cope with the dynamics either of the network and the values being aggregated and thus have to integrate specific solutions to deal with them. The contribution of this paper is to analyze and compare three different solutions to handle network and values dynamics in gossip-based aggregation schemes: (i) an epoch-based approach based on periodic restarts, (ii) an optimized epoch-based approach based on concurrent aggregation threads and (iii) an original approach based on values evaporation that does not require periodic restarts. Experimental results show that our proposal is effective and often more accurate than epoch-based techniques

2009 - Knowledge Networks for Pervasive Services [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Rosi, Alberto; Zambonelli, Franco; M., Baumgarten; M., Mulvenna
abstract

Technologies to pervasively acquireinformation about the physical and socialworlds – as needed by services to achievecontext-awareness – are becomingincreasingly available. Paradoxically, the riskis to make pervasive services overwhelmed bygrowing amounts of contextual data, andunable to properly exploit them. This calls forspecific approaches to automatically organizeand aggregate such data before delivering itto services. Contextual data items should forma sort of self-organized ecology within whichthey autonomously link and combine with eachother into sorts of “knowledge networks”.This can produce compact and easy-to-bemanagedhigher-level knowledge aboutsituations occurring in the environment, andeventually can make services able to easilyacquire “situation-awareness”. In this paper,after having framed the key concepts andmotivations underlying “situation-awareness”and our “knowledge networks” approach, wepresent the design and implementation of a“knowledge networks” prototype, intended asa tool to support self-organization and selfaggregationof contextual data item and tofacilitate their exploitation by pervasiveservices. A representative case study in thearea of adaptive pervasive advertisement isintroduced to clarify the concepts expressed,to exemplify the actual functioning of thetoolkit and of some specific algorithmsintegrated within it, as well as to evaluate itseffectiveness.

2008 - Context-Aware Coordination in the Sensors' Continuum [Articolo su rivista]
Bicocchi, Nicola; Castelli, Gabriella; Rosi, Alberto; Zambonelli, Franco
abstract

Pervasive computing technologies such as sensor networks and RFID tags will soon densely pupulate our everyday environments. These, together with the increasing diffusion of geospatial Web 2.0 tools such as GoogleEarth, will soon form the basis of a shared distributed information space capable of producing and storing data about the physical and social worlds and their processes. This opens up the possibility of exploiting such information space as a general-purpose coordination infrastructure to facilitate users in gathering information about the world, interact with it in a context-aware way, and coordinate with each other via the mediation of that infrastructure. However, the extremely distributed and heterogeneous nature of such infrastructure and the potentially incredible density of the information produced within (at the very extreme, a spatio-temporal continuum of information), introduces several issues related to the management of such infrastructure, i.e., the need for properly aggregating data to abstract from its actual density and to enable multilevel views, and the need for representing data in a simple, uniform, and easy to be managed way. In this paper, after having sketched our general vision for such future coordination infrastructure, we analyse and discuss the key research chalenges to data aggregation and data representation, and present our current research and experimental activity in these areas.

2008 - Pervasive Self-Learning with multi-modal distributed sensors [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Prati, Andrea; Cucchiara, Rita; Zambonelli, Franco
abstract

Truly ubiquitous computing poses new and significantchallenges. One of the key aspects that will condition theimpact of these new tecnologies is how to obtain a manageablerepresentation of the surrounding environment startingfrom simple sensing capabilities. This will make devicesable to adapt their computing activities on an everchangingenvironment. This paper presents a frameworkto promote unsupervised training processes among differentsensors. This framework allows different sensors to exchangethe needed knowledge to create a model to classifyevents. In particular we developed, as a case study,a multi-modal multi-sensor classification system combiningdata from a camera and a body-worn accelerometer to identifythe user motion state. The body-worn accelerometerlearns a model of the user behavior exploiting the informationcoming from the camera and uses it later on to classifythe user motion in an autonomous way. Experimentsdemonstrate the accuracy of the proposed approach in differentsituations.

2008 - Supporting Location-Aware Services for Mobile Users with the Whereabouts Diary [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Rosi, Alberto; Zambonelli, Franco
abstract

Modern handheld devices provided with localization capabilities could be used to automatically create a diary of user's whereabouts, and use it as a complement of the user profile in many applications. In this paper we present the Whereabouts diary, an application/service to log the places visited by the user and to label them, in an automatic way, with descriptive semantic information. In particular, Web-retrieved data and the temporal patterns in which places are visited can be used to define such meaningful semantic labels. In this paper, we describe the general idea at the basis of our service and discuss our implementation and the associated experimental results. In addition, we illustrate an application that can fruitfully exploit the whereabouts diary as a supporting service, and discuss areas for future work.

2007 - Autonomic communication learns from nature [Articolo su rivista]
Bicocchi, N.; Zambonelli, F.
abstract

Autonomic communication focuses on distributed systems and management of network resources at both the infrastructure level and the user level. It is distinct from autonomic computing, which is more oriented toward application software and management of computing resources, although both share the same goals. Autonomic communication research probes into fundamental rethinking of communication, networking, and distributed computing paradigms, to deal with the complexities and dynamics of modern networks. Many researchers, including the authors, are looking to self-organization in nature, such as in colonies of insects for lessons they can apply to self-organizing autonomic communication networks.

2007 - Coordination in the Sensor’s Continuum [Relazione in Atti di Convegno]
Bicocchi, Nicola; Castelli, Gabriella; Rosi, Alberto; Zambonelli, Franco
abstract

The imminent mass deployment of pervasive computing technologies, together with the increasing access of participatory web tools, will soon make available an incredible amount of information about the physical and social worlds and their processes. This opens up the possibility of exploiting all such information space for the provisioning of pervasive context-aware services, for facilitating users in gathering information about the world, coordinating with it, and coordinating with each other via the mediation of an information space.In this paper we present our current research work in this direction, in particular with regard to self- organized data aggregation, data representation and access middleware infrastructure. Due to the unpredictable density of such information spaces, we will also outline the continuum abstraction.

2007 - Self-organizing Spatial Regions for Sensor Network Infrastructures [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

This paper focuses on sensor networks as shared environmental infrastructures, and presents an approach to enable a sensor network to self-partition itself, at pre-defined energy costs, into spatial regions of nodes characterized by similar patterns of sensed data. Such regions can then be used to aggregate data on a per-region basis and to enable multiple mobile users to extract information at limited and pre-defined costs.

2007 - Self-organizing knowledge networks for pervasive situation-aware services [Relazione in Atti di Convegno]
Baumgarten, M.; Bicocchi, N.; Kusber, R.; Mulvenna, M.; Zambonelli, F.
abstract

Adapting to current context of usage is of fundamental importance for pervasive computing services. As the technology for acquiring contextual information is increasingly available and as it is producing growing amounts of data, there is the need for tools to organize such data before delivering it to services. This produces a sort of "knowledge networks " representing comprehensive knowledge related to a "situation " in an expressive yet manageable way. In this paper, also with the help of a simple case study, we motivate the need for situation-awareness and for knowledge networks, introduce a reference architecture for knowledge networks, and exemplify a prototype implementation thereof. Finally, current and future research directions are discussed.

2007 - Supporting situation-aware services with virtual macro sensors [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

Next-generation communication services will be required to adapt their behavior to the specific characteristics of the physical and social environment in which they will be invoked. The technology to acquire contextual information will be increasingly available, e.g., in the form of highly-pervasive sensor networks infrastructure. Indeed, such infrastructure can lead to the production of overwhelming amounts of information, difficult to be managed and interpreted by services. This calls for proper solutions to enable services to extract meaningful general-purpose data from distributed sensors in a compact way. The approach presented in this paper relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence of spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. This makes it possible for services to gather information about the surrounding world as if it was generated by a limited number of virtual macro sensors, independently of the actual structure and density of the underlying sensing infrastructure.

2007 - Towards Self-organizing Virtual Macro Sensors [Relazione in Atti di Convegno]
Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco
abstract

The future mass deployment of pervasive and dense sensor network infrastructures calls for proper mechanisms to enable extracting general-purpose data from them at limited costs and in a compact way. The approach presented in this paper relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence of spatial regions characterized by similar sensed patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. This makes it possible to perceive the network as if it were composed of a limited number of virtual macro sensors, a feature which promises to be very suitable for a number of incoming usage scenarios.

2006 - Intelligent Person-Centric Services for Smart Environments: 'Where are you?' [Relazione in Atti di Convegno]
Chris, Nugent; Matthias, Baumgarten; Maurice, Mulvenna; David, Craig; Zambonelli, Franco; Mamei, Marco; Bicocchi, Nicola; Kevin, Curran
abstract

This paper introduces novel techniques for person-centric services in pervasive spaces. These are focused on the support of independent living spaces for people with mild cognitive impairment, for example. We demonstrate from a technical perspective, how such services could be realised based on the emerging concepts of a distributed network of knowledge, facilitating dynamically composable and flexible service provision that engenders service continuity - beyond the home for example.

2006 - Mechanisms of self-organization in pervasive computing [Relazione in Atti di Convegno]
Bicocchi, N.; Mamei, M.; Zambonelli, F.
abstract

The mass deployment of sensors and pervasive computing systems expected in the next few years, will require novel approaches to program and gather information from such systems. Suitable approaches will be general purpose, independent of a specific scenario and sensor deployment, and able to adapt autonomically to different scales and to a number of unforeseen circumstances. This paper focuses on the requirements and issues of upcoming pervasive computing scenario, and surveys current research initiatives to deal with them. In particular researches addressing data retrieval and aggregation, macro-programming, and data integration in pervasive computing infrastructures will be detailed. Overall, the paper illustrates our ideas on collecting information from both sensor systems and Web resources and on linking them together in overlay knowledge network offering applications comprehensive and understandable information about their computational environment.

2006 - Towards Self-organizing Knowledge Networks for Smart World Infrastructures [Articolo su rivista]
M., Baumgarten; Bicocchi, Nicola; K., Curran; Mamei, Marco; M. D., Mulvenna; C. D., Nugent; Zambonelli, Franco
abstract

Current society is witnessing an age of computing ubiquity where the digital world is not longer limited to closed work, home or social environments but increasingly envelops every aspects of private and social life and their surroundings. However, if computing power is to serve us, and the converse is to be denied, then individual components and their rich panoply of services must be able to operate without significant intrusion. To achieve this, such services would require a high degree of supporting knowledge, including knowledge about the social, computational, and physical environments in which they are situated, as well as self-knowledge about their own functioning. While this provides the knowledge with which they can, eventually, manage and configure themselves it does also makes them more self-aware or in short it makes them smarter. However, in order to get ‘smarter’, the environment, its entities and services need some form of properly represented, well correlated and widely accessible repositories, which leads to the concept of knowledge networks which is the focus of this work.