Digital Twins (DTs) are increasingly adopted in Internet of Things (IoT) systems to provide real-time virtual representations of physical assets. Their growing interconnectivity, however, exposes them to cybersecurity threats that can compromise fidelity and reliability. This work-in-progress study evaluates how representative attacks—Denial of Service, packet loss, and message manipulation—affect DT operational metrics: timeliness, reliability, availability, and consistency. Using a simulation framework based on NS-3, MQTT, and Eclipse Ditto, we emulate realistic DT environments and quantify the resulting degradations. Results show that cyber-attacks leave measurable deviations in these metrics, and that a revised Overall Digital Twin Entanglement (ODTE) score calibrated on normal operation effectively amplifies such anomalies. Metric-based observability thus emerges as a lightweight, non-intrusive approach for early detection and resilience assessment in DT-enabled infrastructures.

This paper surveys the integration of Digital Twins (DT) and Federated Learning (FL) in Industrial IoT (IIoT), highlighting opportunities for real-time monitoring, predictive analytics and distributed intelligence. We review state-of-the-art approaches, identify technological and methodological challenges and discuss how DT and FL can be jointly leveraged to support secure, resilient and adaptive industrial operations. Beyond the classical focus on assets and processes, we extend the analysis to Vulnerable Road Users (VRUs) and show how this concept can be transposed into industrial contexts, where workers and operators act as VRUs inside plants, warehouses, and construction sites. In this perspective, DTs combined with FL can provide simulation-driven insights for worker-centric safety, enabling risk prediction, proactive protection and safer coordination between humans, machines and autonomous systems.

The integration of Digital Twins (DTs) and Blockchain technologies represents a promising direction for building trustworthy, auditable, and interoperable industrial systems. Yet, most existing approaches focus on static identity anchoring rather than on the continuous certification of DT state evolution. This paper proposes a novel framework for the dynamic certification of DTs in Industrial Internet of Things (IIoT) environments, combining a lightweight, permissioned blockchain with adaptive batching and ordering mechanisms. The proposed architecture connects physical assets, DT models and a blockchain-based certification layer through three coordinated components: a DT Instance Manager, a Smart Contract for state hashing and metadata storage, and Verifier Nodes for crosspeer consistency checking. A complete experimental campaign evaluates certification latency, drop rate, commit ratio, and energy overhead under realistic IIoT network conditions. Results demonstrate sub–30ms end-to-end latency for full IIoT emulation and up to 60% energy savings with micro-batching, confirming the feasibility of scalable and energy-aware DT certification across the edge–cloud continuum.

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

In this research, we propose automating network management through data-driven intelligence, with a particular focus on anomalies and network traffic during specific events or periods. We analyze a large dataset collected by Orange mobile network operator in France with the goal of forecasting mobile demand for different classes of services. To model the underlying network infrastructure, we introduce a model for the underlying network based on a hierarchy of virtualization layers and slices. Building on this model, we propose algorithms to optimize the resources allocated to network slices and traffic distribution within the operator’s network. Network performance is evaluated as the fraction of time the mobile traffic is within the capacity of the network. Our results demonstrate that dynamic reallocation of resources among slices, and dynamic load balancing (traffic shaping) between nodes notably improves network performance. These results provide insights into critical aspects related to future 5G network management.

The Modena Automotive Smart Area (MASA) is a cutting-edge testing environment featuring a variety of dynamic physical assets, including smart cameras, roadside units, and connected vehicles. These assets support numerous digital applications, ranging from real-time safety systems to mobility intelligence and 3D visualization of the MASA area. However, the complexity of the physical environment and the diverse needs of these digital applications necessitate a decoupling strategy to ensure efficient operation. This paper presents the design of the MASA Digital Twin, detailing its hierarchical structure, the associated design challenges, and the technological approaches used in its implementation. The MASA Digital Twin serves as a crucial tool for managing the interplay between physical and digital elements, enabling a more structured and adaptable approach to connected mobility and smart city applications.

Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.

The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.

Industry 5.0 embodies the vision for the future of factories, emphasizing the importance of sustainable industrialization and the role of industry in society, through the key concept of placing the well-being of workers at the center of the production process. Building upon this vision, we propose a new paradigm to design human-centric industrial applications. To this end, we exploit Digital Twin (DT) technology to build a digital replica for each entity on the shop floor and support and augment interaction among workers and machines. While so far DTs in automation have been proposed for machine digitalization, the core element of the proposed approach is the Operator Digital Twin (ODT). In this scenario, biometrics allows to build a reliable model of those operator’s characteristics that are relevant in working contexts. Biometric traits are measured and processed to detect physical, emotional, and mental conditions, which are used to define the operator’s state. Perspectively, this allows to manage and monitor production and processes in an operator-in-the-loop manner, where not only is the operator aware of the state of the plant, but also any technological agent in the plant acts and reacts according to the operator’s needs and conditions. In this paper, we define the modeling of the envisioned ecosystem, present the designed DT’s blue-print architecture, discuss its implementation in relevant application scenarios, and report an example of implementation in a collaborative robotics scenario.Note to Practitioners—This paper was motivated by the problem of designing human-cyber-physical systems, where production processes are managed by concurrently taking into account operators, machines and plant status. This answers the needs of the novel Industry 5.0 paradigm, which aims to enhance social sustainability of modern factories. To this end, we propose an architecture based on digital twins that allows to develop a digital layer, detached from the physical one, where the plant can be monitored and managed. This allows the creation of a digital ecosystem where machines, operators, and the interactions among them are represented, augmented, and managed. We discuss how the proposed architecture can be applied to three relevant scenarios: remote training and maintenance, line operation and line supervision. Moreover, the implementation in a collaborative robotics scenario is presented, to provide an example of the proposed architecture can be implemented in industrial scenarios.

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.

The combination of Fluid Computing with the Internet of Things has enabled dynamic orchestration of tasks and functionalities, enhancing performance and responsiveness. Integrating Digital Twins has bridged the cyber-physical gap and the recent introduction of the concept of Fluid Digital Twins (FDTs) opened to the dynamic reconfiguration of functions and simplified augmentation of physical assets’ capabilities. However, introducing new functions or updating existing ones to improve performance or fix bugs poses significant challenges in validating, testing, and deploying these changes in a production environment without disrupting operations. This paper proposes and experimental evaluate an FDT approach for dynamic function management by spawning twin replicas for testing and automatically synchronizing data between production and validation instances.

This paper makes a compelling case for the adoption of the recently proposed Data Mesh architecture within IoT-Edge-Cloud Continuum scenarios, particularly in the context of Intelligent Transportation Systems and Data-driven Mobility Services. Unlike centralized cloud-based approaches, based on data warehouses/lakes connected with ETL (Extract, Transform, and Load) pipelines, Data Mesh promotes a decentralized data ownership model which brings several advantages in addressing open challenges in IoT-Edge-Cloud Continuum scenarios. First, we present an overview of the Data Mesh concepts, and how they advance the state of the art in data management architectures. Secondly, we discuss how their adoption might ease the development of IoT -Edge-Cloud applications in terms of: (i) hiding the heterogeneity of the IoT Layer, (ii) mitigating latency by enabling full domain migrations, and (iii) promoting the adoption of AI techniques, such as MLOps and Federated Learning at the edge of the net-work. Finally, we provide practical guidelines for implementing such an architecture to enhance the safety of pedestrians and vulnerable users, based on our experience with the Modena Automotive Smart Area.

This paper addresses the enduring issue of spam, scams, and robocalls within the telecommunications sector. The diffusion of generative AI technologies has escalated these challenges, as advancements in natural language processing and related tools enhance the sophistication of scams, facilitating the implementation of convincing social engineering attacks. The economic impact of these nefarious activities is significant, as evidenced by the vast number of spam calls and robocalls generated every day that lead to significant financial losses. Although technologies such as blocklists, STIR/SHAKEN, and Caller ID Verification methods are being implemented, the adoption of these solutions by phone companies remains slow due to industry barriers and varied regulatory frameworks. This paper evaluates the effectiveness of current anti-spam countermeasures and highlights the practical limits of these solutions, underscoring the need for improved decision-making tools.

Enhancing the safety of Vulnerable Road Users (VRUs) poses a significant research challenge in the context of connected mobility and a plethora of technological opportunities trying to balance efficiency and widespread applicability. This paper presents a VRUs’ safety application focused on applying 5G Multi-Access Edge Computing (MEC), commercial mobile devices, public cellular networks, and data fusion between vehicle positioning and city camera infrastructure. The application showcases the designed system and its experimental evaluation in the Modena Automotive Smart Area (MASA) through an experimental 5G MEC infrastructure to build a secure and efficient connected mobility environment.

Local Energy Communities are becoming key actors in the panorama of sustainable development. One of the biggest challenges for such communities is to become self-efficient, thanks to an efficient management of the balancing between produced and consumed energy. In order to achieve this goal, it is necessary to design and implement forecasting models that can provide accurate estimates to be subsequently used by optimization and planning algorithms. In this work, we show how neural networks, and in particular long short-term memory networks, can be used to this aim, highlighting an interesting trade-off between the computational requirements and the forecasting accuracy induced by learning different models for clusters of users.

This paper explores the integration of Digital Twins (DTs) in Industry 4.0 and 5.0, highlighting their role in enhancing intelligent, collaborative industrial ecosystems. By representing processes, machinery, operators, and products, DTs enable comprehensive life-cycle support and improved shop-floor operations. Intelligent applications and services can harness DTs as structured and interoperable virtual replicas, entrusted with the responsibility of interfacing with the physical world and facilitating access and mediation of interactions therein. Our study proposes structured DT modeling in industrial ecosystems to demonstrate how DTs enable an effective decoupling of responsibilities and capabilities supporting precise monitoring and data synthesis, optimizing production workflows and maintenance. We discuss DTs’ potential in industrial quality control, highlighting efficiency gains and operational improvements in electric motor production through case studies.

R4.2 In the Industry 4.0 era, Digital Twins (DTs) serve as virtual representations of physical objects and intermediaries between the physical world and the digital realm. DTs require proper modeling, design, and development to ensure their seamless integration along the cloud-to-edge continuum. In particular, this work introduces a microservices-based and serverless-ready model for DTs, laying the foundation for cost-effective DT deployment and orchestration. The joint adoption of microservices and serverless computing offers significant potential to address various challenges, including accommodating variable application requirements, managing load imbalances, and mitigating network faults. The proposed DT model has been implemented in different flavors: two serverless implementations—one that relies on a serverless framework of a cloud provider and one running at the edge on-premises—and a microservices one. These implementations have been experimentally evaluated with particular emphasis on the quality of cyber–physical entanglement. This work not only discusses the advantages and drawbacks of different implementations from a qualitative perspective but also quantitatively evaluates them with the in-the-field collection of experimental performance results. Notably, we report that a serverless implementation typically performs an order of magnitude worse than a microservices one in terms of entanglement, i.e., hundreds vs. tens of milliseconds.

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim at promoting early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

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.

Enhancing the safety of Vulnerable Road Users (VRUs) poses a significant research challenge in the context of connected mobility and a plethora of technological opportunities trying to balance efficiency and widespread applicability. This paper presents a demo focused on applying 5G Multi-Access Edge Computing (MEC) to address this challenge through the combination of commercial mobile devices, public cellular networks, and data fusion between vehicle positioning and city camera infrastructure. The demo showcases the designed system and its experimental evaluation in the Modena Automotive Smart Area (MASA) through the 5G MEC infrastructure of Telecom Italia (TIM) with the aim to build a secure and efficient connected mobility environment.

IoT systems based on Digital Twins (DTs) — virtual copies of physical objects and systems — can be very effective to enable data-driven services and promote better control and decisions, in particular by exploiting distributed approaches where cloud and edge computing cooperate effectively. In this context, digital twins deployed on the edge represents a new strategic element to design a new wave of distributed cyber-physical applications. Existing approaches are generally focused on fragmented and domain-specific monolithic solutions and are mainly associated to model-driven, simulative or descriptive visions. The idea of extending the DTs role to support last-mile digitalization and interoperability through a set of general purpose and well-defined properties and capabilities is still underinvestigated. In this paper, we present the novel Edge Digital Twins (EDT) architectural model and its implementation, enabling the lightweight replication of physical devices providing an efficient digital abstraction layer to support the autonomous and standard collaboration of things and services. We model the core capabilities with respect to the recent definition of the state of the art, present the software architecture and a prototype implementation. Extensive experimental analysis shows the obtained performance in multiple IoT application contexts and compares them with that of state-of-the-art approaches.

Smart homes usually consist of smart objects (SOs) with limited resources and capabilities, and therefore constrain the complexity of applications that can be performed on them. In particular, updating smart objects within a smart home is a challenging undertaking, as seemingly insignificant updates affect the longevity of the deployment if they cause previously established dependencies to break. In this paper, we propose an architecture that we call Longevity Digital Twins (LDTs) as a strategic counterpart of SOs, aimed at running at the edge, as local to the smart home as possible. With this architecture, the capabilities of a SO can be virtually enhanced to support the software update process in the smart home. In this context, foresighted software management requires both a local capability to describe involved functionalities together with awareness about existing dependencies in this distributed system. Using a simulated smart home environment, we first measure the impact of conventional update strategies and then present the noticeable improvement that LDTs offer to this problem. Going further, we present the analysis of a real-world use case that showcases the potential of LDTs on how it could not only prevent the installation of breaking updates but also extend a SOs capabilities and its overall longevity.

Digital Twins (DTs) are becoming a reference design abstraction for many Internet of Things (IoT) application scenarios. Also, data processing is shifting to a decentralised setting leveraging the edge computing paradigm to move computation closer to the physical devices. In this context, Multi-access Edge Computing (MEC) technologies on 5G cellular networks are redefining the IoT networking infrastructure by enabling ultra low latency, and reliable and responsive connectivity. However, evaluation of the MEC architecture from the application developer standpoint is currently missing from literature, as well as an assessment of performance while adopting DT on top of MEC. Therefore, this paper reports on a MEC implementation based on OpenNESS toolkit, in the context of DT-based mobility, and an evaluation of its service-level performance.

The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents a critical element that can significantly limit the design and deployment of DT-oriented applications. This article proposes the Application-driven digital twin networking middleware with the twofold objective of: 1) Simplifying the interaction among heterogeneous devices by allowing DTs to exploit IP-based protocols instead of specialized industrial ones and to enhance packet content expressiveness, by enriching data via well-defined standards. 2) Dynamically managing network resources in edge industrial environments, applying software defined networking to exploit the communication mechanisms most suitable to application requirements, ranging from native IP to more articulated based on packet content.

The estimation of OD flows from mobile phone and GPS positioning data is an important application that can naturally support urban and transport studies. In this work, we first present an approach to generate OD matrices from mobile phone positioning and GPS data, and scale them with traffic counts. Then we compare these matrices, that we consider as an estimate of the potential demand for public transport, with matrices describing the actual routes of public transportation services, that represent the supply. Finally, we present a data driven approach to identifywhereandwhenthe demand for transport is not satisfied. We run experiments with different mobility datasets and the actual public transportation routes in a mid-sized Italian city. In this scenario, our approach allows to detect similar areas of unmatched demand with both such datasets. In particular, two case studies show that the proposed methodology is able to identify two existing bus lines that were recently introduced by the public transport company and local government. Finally, we show an upper bound for the reduced impact of$CO_{2}$emissions, if the unmet demand for transport is entirely shifted to public transport.

The crucial role played by social interactions between smart objects in the Internet of Things (IoT) is being rapidly recognized by the social IoT (SIoT) vision. In this paper, we build upon the recently introduced vision of Speaking Objects-“things” interacting through argumentation-to show how different forms of human dialogue naturally fit cooperation and coordination requirements of the SIoT. In particular, we show how Speaking Objects can exchange arguments in order to seek for information, negotiate over an issue, persuade others, deliberate actions, and so on, namely, striving to reach consensus about the state of affairs and their goals. In this context, we illustrate how argumentation naturally enables such a form of conversational coordination through practical examples and a case study scenario.

In this paper we sketch a vision of future environments densely populated by smart sensors and actuators - possibly embedded in everyday objects - that, rather than simply producing streams of data, are capable of understanding and reporting, via factual assertions and arguments, about what is happening (for sensors) and about what they can make possibly happen (for actuators). These "speaking objects" form the nodes of a dense distributed computing infrastructure that can be exploited to monitor and control activities in our everyday environment. However, the nature of speaking objects will dramatically change the approaches to implementing and coordinating the activities of distributed processes. In fact, distributed coordination is likely to become associated with the capability of argumenting about situations and about the current "state of the affairs", with the aim of triggering and directing proper distributed "conversations" to collectively reach a future desirable state. Accordingly, we discuss how such a novel vision can build upon some readily available technologies, and the research challenges that it poses. Two case studies are used as exemplary scenarios.

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.

An automatic estimate of the number of attendees to events happening in the city can provide valuable information to geographic information systems and geo-located applications. We present a methodology to estimate the number of events’ attendees from cellular network data. In this work, we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated with the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15% of the actual number of attendees.

Emerging pervasive computing services will typically involve a large number of devices and service components cooperating together in an open and dynamic environment. This calls for suitable models and infrastructures promoting spontaneous, situated, and self-adaptive interactions between components. SAPERE (Self-Aware Pervasive Service Ecosystems) is a general coordination framework aimed at facilitating the decentralized and situated execution of self-organizing and self-adaptive pervasive computing services. SAPERE adopts a nature-inspired approach, in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services and devices, all of which interact in accord to a limited set of coordination laws, or eco-laws. In this article, we present the overall rationale underlying SAPERE and its reference architecture. We introduce the eco-laws--based coordination model and show how it can be used to express and easily enforce general-purpose self-organizing coordination patterns. The middleware infrastructure supporting the SAPERE model is presented and evaluated, and the overall advantages of SAPERE are discussed in the context of exemplary use cases.

As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.

The future large-scale deployment of pervasive sensor network infrastructures calls for mechanisms enabling the extraction of general-purpose data at limited energy costs. The approach presented in this article relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence to 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 a collection 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 about its region and is able to 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 evaluated in both a simulation environment and a real test bed, and quantitatively compared with related works in the area. Current limitations and areas of future development are also discussed.

Future processing, storage and communication services will be highly pervasive: people, smart objects, machines and the surrounding space (all embedding devices such as with sensors, RFID tags etc.) will define a highly decentralized cyber environment of resources interconnected by dynamic networks of networks. As communications will extend to cover any combination of ’people, machines and things’, future networks will be increasingly complex and heterogeneous, yet always endorsed with the challenging task of ensuring end-to-end QoS. This paper proposes the groundwork for an advanced cognitive networking paradigm exploitable in future wired and wireless infrastructures: a decentralized cognitive plane to allow for cross-layer, cross-node and cross-network domain self-management, self-control and self-optimization, while being compatible with legacy management and control systems.

Macro programming a spatial computer is the ability to specify application tasks at a global level while relying on compiler-like software to translate the global tasks into the individual component activities. Bayesian networks can be regarded as a powerful tool for macro programming a spatial computer, such as a dense sensor network, in a variety of data analysis applications. In this article we present our architecture to program a spatial computer by means of a distributed Bayesian network and present some applications we developed over a sensor network testing both inference and anomaly-detection analysis.

Web communities are making available an increasing volume of free, fresh, detailed and powerful information about living people. Among them, the Flickr photo-sharing service offers to researchers a database of several millions of geotagged pictures from users all around the world. Working on that opens the door to the study of meaningful mobility data, where title and description of a geotagged picture represent a mine from which extract labels to detect places and events, and useful information about user trends, behaviors and tastes. Our approach goes in the direction of developing an intelligence and unattended system able to extract and take advantage of up-to-date and spontaneous information embedded with pictures, making cities intelligent and able to reach user expectations. Such system, learning from past touristic user experiences, could make customized recommendations on “where to go”, and “what to see”, to people going to visit touristic places for the first time.

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 becoming more 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 data before delivering them 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” (KNs), they are able to provide 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” toward models of “situation awareness” via proper self-organized “KN” tools, and we introduce a general reference architecture for KNs. Second, we describe the design and implementation of a KN toolkit that we have developed, and we exemplify and evaluate algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed.

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.

Pervasive and mobile computing call for suitable middleware and programming models to support the activities of complex software systems in dynamic network environments. In this article we present TOTA (“Tuples On The Air”), a novel middleware and programming approach for supporting adaptive context-aware activities in pervasive and mobile computing scenarios. The key idea in TOTA is to rely on spatially distributed tuples, adaptively propagated across a network on the basis of application-specific rules, for both representing contextual information and supporting uncoupled interactions between application components. TOTA promotes a simple way of programming that facilitates access to distributed information, navigation in complex environments, and the achievement of complex coordination tasks in a fully distributed and adaptive way, mostly freeing programmers and system managers from the need to take care of low-level issues related to network dynamics. This article includes both application examples to clarify concepts and performance figures to show the feasibility of the approach

The imminent mass deployment of pervasive computing technologies such as sensor networks and RFID tags, together with the increasing participation of the Web community in feeding geo-located information within tools such as Google Earth, 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 for the provisioning of pervasive context-aware services for "browsing the world", i.e., for facilitating users in gathering information about the world, interacting with it, and understanding it. However, for this to occur, proper models and infrastructures must be developed. In this paper we propose a simple model for the representation of contextual information, the design and implementation of a general infrastructure for browsing the world, as well as some exemplar services we have implemented over it

Macro programming a distributed system, such as a sensor network, is the ability to specify application tasks at a global level while relying on compiler-like software to translate the global tasks into the individual component activities. Bayesian networks can be regarded as a powerful tool for macro programming a distributed system in a variety of data analysis applications. In this paper we present our architecture to program a sensor network by means of Bayesian networks. We also present some applications developed on a microphone-sensor network, that demonstrate calibration, classification and anomaly detection

Despite the growing interest in pheromone-based interaction to enforce adaptive and context-aware coordination, the number of deployed systems exploiting digital pheromones to coordinate the activities of situated autonomous agents is still very limited. In this article, we present a simple low-cost and general-purpose implementation of a pheromone-based interaction mechanism for pervasive environments. This is realized by making use of RFID tags to store digital pheromones and by having humans or robots spread/sense pheromones by properly writing/reading RFID tags populating the surrounding physical environment. We exemplify and evaluate the effectiveness of our approach via an application for object-tracking. This application allows robots and humans to find forgotten-somewhere objects by following pheromones trails associated with them. In addition, we sketch further potential applications of our approach in pervasive computing scenarios, discuss related work in the area, and identify future research directions.

Abstract Field‐based coordination is a promising approach to orchestrating the activities of components in a wide range of application scenarios. To implement such an approach, one can rely on distributed tuples injected into a network and then propagated to form field‐like distributed data structures to be sensed by application components. Moreover, to gain the full benefits from such an approach, it is important to enable the distributed tuples to preserve their structures despite the dynamics of the network. In this paper, we show how a variety of self‐maintained distributed tuples for field‐based coordination can be easily programmed in the Tuples On The Air (TOTA) middleware. Several examples clarify the approach, and a case study is detailed throughout the paper to ground the discussion. Eventually, performance data are presented to verify the effectiveness of the approach. Copyright © 2005 John Wiley & Sons, Ltd.

Pheromone-based multiagent interaction has received a growing attention in the past few years. Still, so far, the number of deployed systems exploiting pheromones for coordinating activities of distributed agents/robots situated in physical environments has been very limited. In this context, this paper presents a real-world, low- cost and general-purpose, implementation of pheromone interaction, realized by making use of RFID tags technology. Humans and robots can spread/sense pheromones by properly writing/reading RFID tags that are likely to populate our everyday environments. The proposed solution is tested and evaluated via an application for object-tracking, allowing robots and humans to find "forgot-somewhere" objects. The application works by letting objects spread digital pheromones trails that can be tracked afterwards. The paper presents several experiments to assess the effectiveness of our approach, outlines its limitations, and sketches further potential application scenarios.

Predicting the behavior of complex decentralized pervasive computing systems before their deployment in a dynamic environment, as well as being able to influence and control their behavior in a decentralized way, will be of fundamental importance in the near future. In this context, this paper describes the general behavior observed in a large set of asynchronous cellular automata when external perturbations influence the internal activities of cellular automata cells. In particular, we observed that stable macrolevel spatial structures emerge from local interactions among cells, a behavior that does not emerge when cellular automata are not perturbed. Similar sorts of macrolevel behaviors are likely to emerge in the context of pervasive computing systems and need to be studied, controlled, and possibly fruitfully exploited. On this basis, the paper also reports the results of a set of experiments, showing how it is possible to control, in a decentralized way, the behavior of perturbed cellular automata, to make any desired patterns emerge.

Pervasive computing calls for suitable middleware and programming models to deal with large software systems dived in dynamic mobile network environments. Here we present the programming model of TOTA ("tuples on the air"), a middleware for supporting adaptive context-aware activities in pervasive computing scenarios. The key idea in TOTA is to rely on spatially distributed tuples, propagated across a network on the basis of application-specific rules, for both representing contextual information and supporting uncoupled interactions between application components. As shown with the help of a case study scenario, TOTA promotes a simple programming model and can effectively facilitate access to distributed information, navigation in complex networks, and achievement of complex coordination tasks in a fully distributed and adaptive way.

Coordinating the activities of distributed autonomous entities challenges traditional approaches to distributed coordination and calls for new paradigms and supporting middleware. This paper focuses on the problem of orchestrating the movements of mobile autonomous agents in a large-scale distributed systems, and proposes an approach that takes inspiration from physics. Our idea is to have the movements of agents driven by force fields, generated by the agents themselves and propagated via some infrastructure. A globally coordinated and self-organized behavior in the agent's movements can then emerge due to the interrelated effects of agents following the shape of the fields and dynamic fields re-shaping. The approach is presented and its effectiveness described with regard to a concrete case study in the area of urban traffic coordination.

One of the main issues in mobile services' research (M-service) is supporting M-service availability, regardless of the user's context (physical location, device employed, etc.). However, most scenarios also require the enforcement of context-awareness, to dynamically adapt M-services depending on the context in which they are requested. In this paper, we focus on the problem of adapting M-services depending on the users' location, whether physical (in space) or logical (within a specific distributed group/application). To this end, we propose a framework to model users' location via a multiplicity of local and active service contexts. First, service contexts represent the mean to access to M-services available within a physical locality. This leads to an intrinsic dependency of M-service on the users' physical location. Second, the execution of service contexts can be tuned depending on who is requesting what M-service. This enables adapting M-services to the logical location of users (e.g., a request can lead to different executions for users belonging to different groups/applications). The paper firstly describes the framework in general terms, showing how it can facilitate the design of distributed applications involving mobile users as well as mobile agents. Then, it shows how the MARS coordination middleware, implementing service contexts in terms of programmable tuple spaces, can be used to develop and deploy applications and M-services coherently with the above framework. A case study is introduced and discussed through the paper to clarify our approach and to show its effectiveness.