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.

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is "fluid", since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework, and prove it is self-stabilising and locally optimal. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling while maintaining a tuneable trade-off between accuracy and efficiency.

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.

The increasing complexity and unpredictability of many ICT scenarios will represent a major challenge for future intelligent systems.The capability to dynamically and autonomously adapt to evolving and novel situations, with a partial or limited knowledge of the domain, both at the level of individual components and at the collective level, will become a crucial need for smart devices acting in many application domains.In this paper, we envision future systems able to selfdevelop mental models of themselves and of the environment they act in.Key properties will include: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act in the presence of others, i.e., at the collective level.In our work, we will introduce the vision of self-development in ICT systems, by framing its key concepts and by illustrating suitable application domains.Then, we overview the many research areas that are contributing or can potentially contribute to the realisation of the vision, and identify some key research challenges.

The increase in computational and communication power of pervasive devices is also enabling to embed increasing intelligence in devices, there included the capability to act in autonomy, and possibly interacting with each other, in order to achieve specific goals, thus leaving humans out of the decision loop. In this context, the goal of the panel is thus reasoning about the possible implications (technical, ethical, and lega) of assigning great and often critical decision power to pervasive autonomous systems.

This short paper reports the summary of the panel held in the context of the PerCom 2022 conference.

Our streets will be soon populated by multitudes of autonomous (i.e., self-driving) vehicles, calling for appropriate solutions to coordinate their collective movements in order to ensure safety and efficiency. In particular, crossing intersections can be based on a number of different coordination approaches, from traditional ones (e.g., traffic lights) to innovative ones (e.g., based on dynamic negotiations between vehicles). In this paper, after having introduced the general issues associated to intersection management in the presence of autonomous vehicles, we show by simulation experiments that no single approach exhibits the best behaviour for all traffic conditions and for all performance indicators. On this basis, we introduce an adaptation mechanism that enables an intersection to dynamically select the most proper coordination approach depending on traffic conditions and the performance indicator to be optimized. Simulation experiments show the effectiveness of such adaptive approach.

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.

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in "space and time" of distributed data structures called fields. More specifically regarding time, in field-based coordination (as in many other distributed approaches to coordination) it is assumed that local activities in each device are regulated by a fair and unsynchronised fixed clock working at the platform level. In this work, we challenge this assumption, and propose an alternative approach where scheduling is programmed in a natural way (along with usual field-based coordination) in terms of causality fields, each enacting a programmable distributed notion of a computation "cause" (why and when a field computation has to be locally computed) and how it should change across time and space. Starting from low-level platform triggers, such causality fields can be organised into multiple layers, up to high-level, collectively-computed time abstractions, to be used at the application level. This reinterpretation of time in terms of articulated causality relations allows us to express what we call "time-fluid" coordination, where scheduling can be finely tuned so as to select the triggers to react to, generally allowing to adaptively balance performance (system reactivity) and cost (resource usage) of computations. We formalise the proposed scheduling framework for field-based coordination in the context of the field calculus, discuss an implementation in the aggregate computing framework, and finally evaluate the approach via simulation on several case studies.

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.

Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are used to classify human activities to support applications such as ambient assisted living, health monitoring, and behavioural intervention. Building a robust and meaningful classifier needs annotated ground truth, labelled with what activities are actually being observed—and acquiring high-quality, detailed, continuous annotations remains a challenging, time-consuming, and error-prone task, despite considerable attention in the literature. In this article, we use knowledge-driven ensemble learning to develop a technique that can combine classifiers built from individually labelled datasets, even when the labels are sparse and heterogeneous. The technique both relieves individual users of the burden of annotation and allows activities to be learned individually and then transferred to a general classifier. We evaluate our approach using four third-party, real-world smart home datasets and show that it enhances activity recognition accuracies even when given only a very small amount of training data.

Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support System assisting clinicians in assessing health risk of patients by delivering "Risk Prediction as a Service". By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, and seamless interoperability, while promoting collaboration.

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.

No abstract available.

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.

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.

Pervasive and mobile computing technologies can make our everyday living environments and our cities "smart", i.e., capable of reaching awareness of physical and social processes and of dynamically affecting them in a purposeful way. In general, living in a smart environment and being made part of its activities somehow make us - as individuals - smarter as well, by increasing our perceptory and social capabilities. However, a potential risk could be to start delegating too much to the environment itself, losing in critical attention, abandoning individual decision making for relying on collective computational governance of our activity, and in the end also losing awareness of environmental and social processes. The panel intends to discuss the above issues with the help of relevant researchers in the area of pervasive computing, smart environments, collective intelligence.

The large number of available services, provided by different means such as the Web, smartphone apps, and wearable devices, provides users a valuable support for their everyday activities, but at the same time introduces the need for a tailored choice and exploitation of them. Several approaches have been proposed that take into account users' preferences, but a comprehensive user-aware approach is still missing. In a previous work we proposed an approach that addressed the user-aware composition of services, in this paper we propose to extend the previous approach by considering also semantic techniques and simple collaboration aspects. So, we propose the definition of a middleware for composing and exploiting services that exhibits some key features: (i) it considers the profile of the users that exploit the service to choose appropriate services for them, (ii) it exploits techniques of semantic similarity between user and service descriptions to make the choice more effective, and (iii) it enables the collaboration among users. By means of a case study we present a possible scenario that can take advantage of our middleware, and we show how it can be exploited.

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.

Self-adaptive systems are distributed computing systems composed of different components that can adapt their behavior to different kinds of conditions. This adaptation does not concern the single components only, but the entire system. In a previous work we have identified several patterns for self-adaptation, classifying them by means of a taxonomy, which aims at being a support for developers of self-adaptive systems. Starting from that theoretical work, we have simulated the described self-adaptation patterns, in order to better understand the concrete and real features of each pattern. The contribution of this paper is to report about the simulation work, detailing how it was carried out, and to present a “table of applicability” that complete the initial taxonomy of patterns and provides a further support for the developers.

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 rooted in this scenario. The framework has been designed to collect and classify data streams in a modular way. It supports service oriented, reconfigurable components and provides a solid background to put at joint work specification- and data-driven approaches. Furthermore, we use the framework to experimentally evaluate an innovative meta-classification scheme based on state-automata for (i) improving energy efficiency, (ii) improving classification accuracy and (iii) improving software engineering of aware systems.

A highly decentralized system of autonomous service components consists of multiple and possibly interacting feedback loops. These loops can be organized into a variety of architectural patterns. Although several authors have addressed the need to make feedback loops first-class entities, little attention has been given to providing solid tool support for their engineering and implementation. In this paper, we present SimSOTA - an integrated Eclipse plug-in tool to architect, engineer and implement self-adaptive systems based on our feedback loop-based approach. SimSOTA adopts model-driven development to model and simulate complex self-adaptive architectural patterns, and to automate the generation of Java-based implementation code for the patterns. The approach is validated using a case study in cooperative electric vehicles.

Public display technology (like digital signage) and private one (like smartphones) has advanced significantly in capacity, capability and spread over the past years. For the provision of information services in display technology rich settings we draw the analogy to biological ecosystems as a community of living organisms together with nonliving components of the environment, linked together through information flows, and by that acting as a global system. We identify individuals in the information society with their situational information demands and their implicit and explicit interaction styles as the living, and the plethora of display devices as the non-living part of an information ecosystem. A local interactions oriented service architecture is developed, the SAPERE pervasive service substrate. A situation aware social feedback mechanism is proposed and implemented in the SAPERE architecture, demonstrating how incentivizing local interactions can stand as a principle to attain desirable global information balance.

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.

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.

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.

No abstract available.

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.

Changes and adaptations are always necessary after the deployment of a multi-agent system (MAS), as well as of any other type of software systems. Some of these changes may be simply perfective and have local impact only. However, adaptive changes to meet new situations in the operational environment of the MAS may impact globally on the overall design. More specifically, those changes usually affect the organizational structure of the MAS. In this paper we analyze the issue of design change/adaptation in a MAS organization, and the specific problem of how to properly model/design a MAS so as to make it ready for adaptation. Special attention is paid to the Gaia methodology, whose suitability in dealing with adaptable MAS organizations is also discussed with the help of an illustrative application example.

No abstract available.

To support and engineer the spatial coordination of distributed pervasive services, we propose a chemical-inspired model, which extends tuple spaces with the ability of evolving tuples mimicking chemical systems, that is, in terms of reaction and diffusion rules that apply to tuples modulo semantic match. The suitability of this model is studied by considering a self-adaptive display infrastructure providing people nearby with several visualization services (advertisements, news, personal and social content). The key result of this article is that general-purpose chemical reactions inspired by population dynamics can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction.

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

We describe a prototype of trading system platform populated by agents who autonomously decide to buy and/or sell items according to a set of local needs which arise dynamically (also by possibly accessing information provided by pervasive devices) by in the process of fulfilling a given overall utility. The market has combinatorial nature in a way that items to be traded are combined into packages, in accordance with a principle that drives the nature of many current markets. However, differently from these, items belong to a number of distinct sellers distributed in the platform, and are chosen singularly on the basis of buyers preferences and needs. Agents are thus situation-aware, with sellers coming acquainted of the market demand, and buyers price offers, through a Knowledge Network. This latter drives the way market offers balance the demand by gathering the needed information in an autonomous way and taking advantage of pervasive devices. Packaging is realized by agent aggregation into Virtual Sellers, in an autonomous fashion, and we propose an opportunistic policy whereby aggregation is governed by a Combinatorial Auction. The market is studied through proof-of-concept simulation, where the efficiency deriving from the opportunistic aggregation based on Combinatorial Auctions and the influence of contextual self-awareness are studied.

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.

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

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.

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.

Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.

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.

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization , and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

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.

Objects and agents are abstractions that exhibit points of similarity, but the development of agent-based software poses other challenges to software engineering since software agents are inherently more complex entities. In addition, a large-scale multi-agent system needs to satisfy multiple stringent requirements such as reliability, security, interoperability, scalability, reusability, and maintainability. This workshop brought together researchers and practitioners to discuss the current state and future direction of research in software engineering for large-scale multi-agent systems. A particular interest was to understand those issues in the agent technology that difficult and/or improve the production of large-scale distributed systems.

Information overloading introduced by the large amount of data that is spread over the Internet must be faced in an appropriate way. The dynamism and the uncertainty of the Internet, along with the heterogeneity of the sources of information are the two main challenges for today's technologies related to information management. In the area of information integration, this paper proposes an approach based on mobile software agents integrated in the MOMIS (Mediator envirOnment for Multiple Information Sources) infrastructure, which enables semi-automatic information integration to deal with the integration and query of multiple, heterogeneous information sources (relational, object, XML and semi-structured sources). The exploitation of mobile agents in MOMIS can significantly increase the flexibility of the system. In fact, their characteristics of autonomy and adaptability well suit the distributed and open environments, such as the Internet. The aim of this paper is to show the advantages of the introduction in the MOMIS infrastructure of intelligent and mobile software agents for the autonomous management and coordination of integration and query processing over heterogeneous data sources.

This special issues contains a selection of the best papers presented at the Fifth International Workshop on Cooperative Information Agents, held in Modena, Italy, September 6–8, 2001. Information agent technology has become one of the major key technologies for the Internet and the World Wide Web.2,5 It mainly emerged as a response to the challenges of cyberspace from both the technological and human user perspective. Development of information agents requires expertise from different research disciplines such as Artificial Intelligence (AI), advanced databases and knowledge base systems, distributed information systems, information retrieval, and Human Computer Interaction (HCI). The Fifth International Workshop on Cooperative Information Agents (CIA) continued the tradition by capturing the intrinsic interdisciplinary nature of the above research area by calling for contributions from different research communities, and by promoting open and informative discussions on all related topics. In keeping with tradition, the workshop featured a sequence of regular and invited talks of excellence given by leading experts in the field. As a novelty, the CIA 2001 workshop issued two awards for best paper and best system innovation to acknowledge particularly significant advances in research and development respectively, in the area of information agents. An extended version of the awarded best paper authored by Onn Shehory is included in this special issue.

The design and development of Internet applications requiring dynamic and possibly mobile access to Internet resources can take advantage of an approach based on autonomous mobile agents. However, mobility introduces peculiar issues related to the modeling and management of the agents' coordination activities. This paper introduces context-dependent coordination as a framework for the design and development of Internet applications based on mobile agents, and shows how it can be supported by a proper coordination infrastructure. Context-dependent coordination is centered on the notion of programmable coordination media, as the software abstraction via which an agent in an Internet site can access to local resources and coordinate with local agents. Programmability stems from the fact that the behavior of the media can be fully configured to influence agents' coordination activities. This enables local administrators to configure coordination media so as to enact site-dependent coordination policies, and mobile agents to configure the accessed coordination media to obtain an application-dependent behavior of the media themselves. Several application examples shows that exploiting context-dependent coordination promotes a clear separation of concerns in design and development, and can make applications more modular and easier to be maintained. The MARS system is assumed as an exemplar coordination infrastructure to clarify the concepts expressed and to show their actual implementation.

Mobility introduces peculiar coordination problems in agent-based Internet applications. First, it suggests the exploitation of an infrastructure based on a multiplicity of local interaction spaces. Second, it may require coordination activities to be adapted both to the characteristics of the execution environment where they occur and to the needs of the application to which the coordinating agents belong. This paper introduces the concept of context-dependent coordination based on programmable interaction spaces. On the one hand, interaction spaces associated to different execution environments may be independently programmed so as to lead to differentiated, environment-dependent, behaviors. On the other hand, agents can program the interaction spaces of the visited execution environments to obtain an application-dependent behavior of the interaction spaces themselves. Several examples show how an infrastructure for context-dependent coordination can be exploited to simplify the design of Internet applications based on mobile agents. In addition, the MARS coordination infrastructure is presented as an example of a system in which the concept of context-dependent coordination has found a clean and efficient implementation.

Multi-agent systems can very naturally be viewed as computational organisations. For this reason, we believe organisational abstractions offer a promising set of metaphors and models that can be exploited in the analysis and design of such systems. To this end, the concept of role models is increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts — organisational rules, organisational structures, and organisational patterns — and discuss why we believe they are necessary for the complete specification of computational organisations. In particular, we focus on the concept of organisational rules and introduce a formalism, based on temporal logic, to specify them. This formalism is then used to drive the definition of the organisational structure and the identification of the organisational patterns. Finally, the paper sketches some guidelines for a methodology for agent-oriented systems based on our expanded set of organisational abstractions.

No abstract available.

Owing to its widespread diffusion and ease of use, the Web is an attractive platform to support distributed cooperative work. The paper presents an object-oriented proxy-based framework to support synchronous cooperation on the Web. The framework implements the general-purpose mechanisms of a proxy server. Specific application-dependent functionalities have to be implemented in a module to be installed in the proxy-framework. This leads to the implementation of an extensible proxy-framework, which facilitates the development of specific cooperative services. The paper presents and evaluates several applications enabling different cooperative Web activities, to confirm the effectiveness of our proposal in terms of reusability, usability and performance. Copyright © 1999 John Wiley & Sons, Ltd.

The paper introduces a taxonomy of the possible coordination models for mobile agent applications and evaluates their impact in the design of a widely distributed application in the area of information retrieval. The analysis outlines the advantages of uncoupled coordination models and points out the need for coordination models based on reactive blackboards.

The paper focuses on local load balancing policies for massively parallel architectures and introduces a new scheme for load information exchange between neighbor nodes. The idea is to distort the exchanged load information to let the policy keep into account a more global view of the system and overcome the limits of the local scope. The presented scheme has been integrated into two variants of a direct-neighbor policy and evaluated in dependence of the characteristics of the system load. Experimental results show that the transmission of distorted load information provides high efficiency unless the dynamicity of the load becomes too high, in which case it is preferable to exploit non-distorted load information.

The paper focuses on fault-tolerant distributed computations where processes can take local checkpoints without coordinating with each other. Several distributed online algorithms are presented which avoid rollback propagation by forcing additional local checkpoints in processes. The effectiveness of the algorithms is evaluated in several application examples, showing their limited capability of bounding the number of additional checkpoints.

The tuple space abstraction is a powerful and general coordination model for parallel and distributed programming. However, this model is based on the abstraction of a global space, difficult to implement in distributed memory parallel systems with high-performance and scalability. The paper discusses the possible distribution strategies to implement a tuple space server and proposes a new replication policy suitable for massively parallel systems. The proposed strategy achieves scalability by organising the system in a hierarchical way and by integrating coherence protocols of limited overhead. Moreover, the chosen organisation encourages the presence of multiple tuple spaces each with a constrained scope. The paper describes and analyses a transputer-based implementation: the hierarchical organisation of the tuple space makes the access time proportional to the locality of the references and bounded by the logarithm of the system size. © 1998 John Wiley & Sons, Ltd.

Cellular automata are used for designing high performance applications in many areas. The paper describes CARPET, a high level programming language based on the cellular automata model. CARPET is a programming language designed to support the development of parallel high performance software. It exploits the computing power of a highly parallel computer releasing a user from using explicit parallel constructs. A CARPET implementation has been used for programming cellular algorithms in the CAMEL parallel environment. With CARPET, a user might write programs to describe the actions of thousands of simple active agents interacting locally, then the CAMEL environment allows a user to observe the global complex evolution that arises from their parallel execution and their local interactions.

The paper presents the Abstract Configuration Language (ACL) implemented within the Parallel Objects object oriented parallel programming environment. ACL defines a set of directives that allow users to specify the allocation needs of his/her application components without being aware of the architectural details. ACL directives drive the allocation decisions of the run time support, by adapting its general purpose behaviour to follow an applications particular allocation needs. The effectiveness of the ACL approach in increasing the performances of parallel applications is confirmed by a testbed application.