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In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.

Wearable devices and wireless communications, combined with personalized health management, are future trends that healthcare practices and procedures are heading toward. To facilitate this progress, new technologies and methods are required to provide reliable measurements, end-to-end communications, and data analysis mechanisms from the data source to medical health records. The wireless body area network (WBAN) is the major element in this process. This Special Section is not only limited to on-body WBAN devices but also focuses on benefitting technologies which can distribute vital information inside a human body or allow control of implantable gadgets. Dependable wireless communications combined with versatile application areas, such as accurate localization or behavior analysis techniques, remote monitoring, adoption of vital sensors, and actuators, can benefit the increased use of new WBAN technologies in various healthcare-related studies. In the end, this will make healthcare processes more effective and user friendly, and simultaneously increase the safety of (out)patients. This Special Section focuses on various theoretical and experimental views on WBAN applications, technologies, implementations, and utilizations based on invited, extended versions of the best-evaluated articles from BodyNets 2018, held at Oulu, Finland, in October 2018, as well as quality articles submitted from the Open Call. This Special Section includes 16 accepted articles which have undergone a rigorous peer-review process. Three of the accepted articles were invited extensions of the articles presented at the BodyNets 2018 conference. The total number of submissions we received for this Special Section was 45; thus, the acceptance rate was 36%.

Software agents have been exploited to handle the inherent dynamicity in the Internet of Things (IoT) systems, as agents are capable of autonomous, reactive and proactive operation in response to changes in their local environment. Agents, operating at the network edge, enable leveraging cloud resources into the proximity of the user devices. However, poor interoperability with the existing IoT systems and the lack of a systematic methodology for IoT system development with the agent paradigm have hindered the utilization of software agent technologies in IoT. In this paper, we describe the development process and the system architecture of a mobile crowdsensing service, provided by an agent-based smart object that comprises agents in both edge and user devices. Mobile crowdsensing is an example of such an application that relies on large-scale participatory sensor networks, where participants have active roles in producing information about their environment with their smartphones. This scheme introduces challenges in handling dynamic opportunistic resource availability, due to mobility and unpredicted actions of the participants. We present how ACOSO-Meth (Agent-oriented Cooperative Smart Object-Methodology) guidelines the development process systematically from the analysis to the actual agent-based implementation of a crowdsensing service. The implementation is done with the ROAgent framework that utilizes resource-oriented architecture and REST principles to integrate agent-based smart objects seamlessly with the programmable Web.

The Special Section in IEEE Access on Body Area Networks (BAN) collects the extended versions of the best manuscripts submitted to and presented at “Bodynets 2016,” a conference that “aims to provide a world-leading and unique opportunity for bringing together researchers and practitioners from diverse disciplines to plan, analyze, design, build, deploy and experiment with/on Body Area Networks (BANs)” http://bodynets.org/2016/show/home, held in December 2016 in Torino, Italy.

The Agent-based Cooperating Smart Objects methodology (ACOSO-Meth) fully supports the systematic development of Internet of Things (IoT) systems from analysis to implementation by tackling their manifold requirements (e.g., self-management, distributed smartness, interoperability). At the same time, ACOSO-Meth allows the re-engineering of existing IoT systems, thus enhancing their maintainability, reusability and extensibility. In such direction, this paper (i) first presents the integration of the resource-oriented agent framework complying with the IETF Constrained RESTful Environment (CoRE) framework into ACOSO-Meth; then (ii) reports a case study to exemplify the re-engineering of a resource-constrained agent application through the ACOSO-Meth metamodel-driven approach.

La méthode DEXi-Pépi a été développée afin d’avoir une approche globale d’amélioration environnementale, économique et sociale des systèmes de culture hors sol de pépinière. Cette méthode, adaptée du modèle DEXiPM®, est un modèle hiérarchique multicritère et qualitatif qui comporte 89 critères d’entrée : (i) 56 critères dépendants du système de culture, (ii) 23 critères liés au contexte et dépendant du système et (iii) 10 critères liés au contexte et indépendant du système. Plusieurs critères sont redondants dans l’évaluation (26). Le nombre de critère d’entrée par pilier de la durabilité est déséquilibré, reflétant en partie le déséquilibre des connaissances sur certains piliers. Le modèle DEXi-Pépi est un prototype qui est amené à évoluer au fur et à mesure des utilisations et en fonction des attentes des utilisateurs., The DEXi-Pépi method was developed in order to have a global approach for improving aboveground nursery culture systems. This method, adapted from DEXiPM® model, is a hierarchical multi-criteria and qualitative model that includes 89 entry criteria: (i) 56 criteria dependent of the culture system, (ii) 23 criteria linked to the context and system-dependent and (iii) 10 criteria linked to the context and independent of the system. Several criteria are redundant in the assessment (26). The number of entry criteria for each pillar of sustainability is unbalanced, partly reflecting the imbalance of knowledge on some pillars. The DEXi-Pépi model is a prototype that will evolve as the use increases and according to the users’ expectations.

The main technical underpinnings of Cloud Computing infrastructures and services include virtualization, service-orientation, elasticity, multi-tenancy, power efficiency, and economics of scale. The perceived advantages for Cloud-service clients include the ability to add more capacity at peak demand, reduce cost, experiment with new services, and to remove unneeded capacity. This special issue came to be due to the direct and indirect involvement of many researchers, academicians, developers, designers, and industry practitioners. As the volume of data produced at the Internet surges, the pub/sub paradigm becomes increasingly popular for content access and dissemination. The authors focus on content-centric pub/sub networks and describe a new placement and replica assignment algorithm. The authors propose a novel distributed caching scheme where caches are organized in domains so as to fetch content from other caches and share content between caches in the domain. 55 18 3987 3990 Cited By :1

The overall objective of PURE is to provide practical IPM solutions to reduce dependence on pesticides in selected major farming systems in Europe. This paper summarises methodological advances with regards to the design and assessment of IPM solutions. The presented case studies include major crops (cropping systems based on wheat or maize), field vegetables, orchards, vineyard and Controlled Environment Agriculture systems. - Ecological modelling. A software package (Universal Simulator) for collaborative ecological modelling is now available:http://www.ecolmod.org/. - Modelling for ex-ante and ex post assessment of IPM solutions. A multi-criteria model (DEXIPM) for sustainability assessment of innovative crop protection strategies has been developed along with SYNOPS, a web-based model for scaling up ex-post pesticide risk assessments at the individual crop level to the farm and regional levels. In addition, a model for ex-ante evaluation of IPM solutions is currently under development specifically for orchards (PREMISE). - Multiple pest modelling. An interactive generic modelling platform to help design models that simulate yield losses caused by an injury profile in a given production situation (X-PEST) is currently under development. Moreover, theoretical mathematical modelling approaches are conducted to represent the interactions between generalist biological control agents and multiple pests. - Optimisation techniques. Reinforcement learning methods have been adapted and applied to IPM. Multiobjective optimisation algorithms for model-based design of IPM solutions are being developed. The Graph based Markov Decision Process framework is being used for the optimisation of sequential decisions under uncertainty in a spatial context. - Cost-benefit analysis and consumers' willingness to pay. Cost-benefit analyses are conducted for IPM solutions tested in the PURE project. An experimental approach is planned to characterise consumers' willingness to pay for agricultural goods produced under IPM solutions as a function of their level of information on the mode of production. It is important to state that the methodological breakthroughs produced in this work package will not only benefit to the PURE project, but also aim at contributing to the design of practical IPM solutions to reduce dependence on pesticides for a wider range of farming systems. This is made possible by ensuring as much as possible genericity in the developed approaches.

Recent years have seen rapid advancements in wireless sensor networks (WSNs) and software agents resulting, among others, in maturation of their technology platforms. Furthermore, benefits of combining these research areas have been analyzed. The MAPS agent platform allows fusion of agents and WSNs. However, due to the hardware limitation, MAPS misses important functionalities needed, for instance, in advanced decision support. Such functions are available, among others, in the JADE agent platform, geared towards more powerful computing devices. Therefore, integration of MAPS and JADE had to be considered. The aim of this paper is to discuss technical issues involved in achieving this goal.

A number of scientific and technological advancements enabled turning the Internet of Things vision into reality. However, there is still a bottleneck in designing and developing IoT applications and services: each device has to be programmed individually, and services are deployed to specific devices. The Fluidware approach advocates that to truly scale and raise the level of abstraction a novel perspective is needed, focussing on device ensembles and dynamic allocation of resources. In this paper, we motivate the need for such a paradigm shift through three case studies emphasising a mismatch between state of art solutions and desired properties to achieve.

With the large diffusion of Business Process Managemen (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are defined at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation.

This article describes the architecture of a monitoring component for the YAWL system. The architecture proposed is based on sensors and it is realized as a YAWL service to have perfect integration with the YAWL systems. The architecture proposed is generic and applicable in different contexts of business process monitoring. Finally, it was tested and evaluated in the context of risk monitoring for business processes.

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for 'pulverization': applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

New technologies are transforming medicine, and this revolution starts with data. Health data, clinical images, genome sequences, data on prescribed therapies and results obtained, data that each of us has helped to create. Although the first uses of artificial intelligence (AI) in medicine date back to the 1980s, it is only with the beginning of the new millennium that there has been an explosion of interest in this sector worldwide. We are therefore witnessing the exponential growth of health-related information with the result that traditional analysis techniques are not suitable for satisfactorily management of this vast amount of data. AI applications (especially Deep Learning), on the other hand, are naturally predisposed to cope with this explosion of data, as they always work better as the amount of training data increases, a phase necessary to build the optimal neural network for a given clinical problem. This paper proposes a comprehensive and in-depth study of Deep Learning methodologies and applications in medicine. An in-depth analysis of the literature is presented; how, where and why Deep Learning models are applied in medicine are discussed and reviewed. Finally, current challenges and future research directions are outlined and analysed.

In Smart Factories, automated guided vehicles (AGVs) accomplish heterogeneous tasks as moving objects, restoring connectivity, or performing different manufacturing activities into production lines. These kinds of devices combine several capabilities, as artificial intelligence (visual and speech recognition, automatic fault detecting, proactive behavior) and mobility, into the so-called “mobile intelligence.” A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents, mutually interacting on their behalf. Here, to reach a given target by contemporary satisfying some basic requirements like effectiveness and efficiency, it is often necessary to organize ad hoc teams of free-moving vehicles, sensors, and smart devices. Therefore, a specific issue is the adequate representation of the reciprocal agent/device trustworthiness for advantaging such team formation processes within a smart factory environment. To this end, in this article, first, we define a trust measure based on reliability and reputation of AGVs, which are computed based on the feedbacks released for the AGVs activities in the factory; second, we design a trust framework exploiting the defined measures to support the formation of virtual, temporary, and trust-based teams of mobile intelligent devices; and third, we present a set of experimental results highlighting that the proposed trust framework can improve the workshop performance in terms of effectiveness and efficiency.

Smart cities are built on top of heterogeneous IoT infrastructures, that can be viewed as communities of software agents (the intelligent objects) that interact with each other to realize complex activities. These agents operate on behalf of users that need services; for these reason agents are often in competition with each other. On the other hand, an agent can often benefit from collaborating with other agents in some circumstances, exchanging information and services. Under this viewpoint, the task of finding the best partners to collaborate is a key task for an agent. A general consensus exists about the benefits deriving by forming friendships and groups for mutual cooperation inside competitive Multi-Agent Systems (MASs). In this respect, the existing proposals are usually addressed to maximize the profit at the level of individual agent or group. Unfortunately, the most part of these approaches could advantage the most aggressive agents, also in presence of bad social behaviors. This is not a desired scenario in a smart city environment. A possible solution to this problem is that of promoting correct behaviors and meritocracy inside agent communities. To this aim, we propose to model the competitive MAS scenario in the framework of non cooperative games by assuming to represent (i) the trustworthiness of agents relationships by means of a trust model and (ii) the capability of a community to provide its members with a good environment by means of its social capital. As a result, a group formation algorithm capable to asymptotically maximize the social capital is proposed. This algorithm highlights two main features: (i) the computed solution is a Nash equilibrium in the considered game and (ii) the only rewarded agents are those having the most correct behaviors.

This paper deals with the principal aspects emerging from the application of trust techniques to the IoT domains with respect to the particular viewpoint of an IoT environment. We consider intelligent agents technology to add social behavior to the community of the smart objects, and we analyze the fundamental role of the concepts of trust and reputation in this perspective. Also, we analyze the existing architectures for IoT and explain how to integrate IoT with fog/edge computing. Besides discussing the main proposals present in the literature, the key contribution of this paper consists of providing a comparative study of the main current architectures for modeling trust in IoT environments. In this setting, we propose a comparison based on the important characteristics of the IoT layer and the architecture type. Such an analysis allows us to highlight both advantages and limitations of each approach, and to discuss the emerging research challenges.

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is described by a set of centrality metrics which represent one of the most important results of SNA. Degree, closeness, betweenness and clustering coefficient are the most used centrality measures. Their use is, however, severely hampered by their computation cost. This issue can be overcome by an algorithm called Game of Thieves (GoT). Thanks to this new algorithm, we can compute the importance of all elements in a network (i.e. vertices and edges), compared to the total number of vertices. This calculation is done not in a quadratic time, as when we use the classical methods, but in polylogarithmic time. Starting from this we present our results on the correlation existing between GoT and the most widely used centrality measures. From our experiments emerge that a strong correlation exists, which makes GoT eligible as a centrality measure for large scale complex networks.

The purpose of the next internet of things &#x0028 IoT &#x0029 is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting. The convergence of IoT and multi-agent systems &#x0028 MAS &#x0029 provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine &#x0028 M2M &#x0029 cooperation among smart entities. However, the selection of reliable partners for cooperation represents a hard task in a mobile and federated context, especially because the trustworthiness of devices is largely unreferenced. The issues discussed above can be synthesized by recalling the well known concept of social resilience in IoT systems, i.e., the capability of an IoT network to resist to possible attacks by malicious agent that potentially could infect large areas of the network, spamming unreliable information and &#x002F or assuming unfair behaviors. In this sense, social resilience is devoted to face malicious activities of software agents in their social interactions, and do not deal with the correct working of the sensors and other information devices. In this setting, the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities. In this paper, we propose a framework for agents operating in an IoT environment, called ResIoT, where the formation of communities for collaborative purposes is performed on the basis of agent reputation. In order to validate our approach, we performed an experimental campaign by means of a simulated framework, which allowed us to verify that, by our approach, devices have not any economic convenience to performs misleading behaviors. Moreover, further experimental results have shown that our approach is able to detect the nature of the active agents in the systems &#x0028 i.e., honest and malicious &#x0029 , with an accuracy of not less than 11 &#x0025 compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.

In this paper, we are interested in answering the following research question: &#x201C Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities? &#x201D In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community &#x0028 called global reputation &#x0029 , we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the Gk index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.

The present study investigates the extent to which robots' 1) degree of human likeness, 2) gender and 3) ethnicity affect elders' attitude towards using robots as healthcare assistants. To this aim 2 groups of 45 seniors, aged 65 + years, were asked to watch video clips showing three speaking female and male robots, respectively. Each set of stimuli consisted in 2 androids, one with Caucasian and one with Asian aspect, and 1 humanoid robot. After each video clip elders were asked to assess, through the Robot Acceptance Questionnaire (RAQ) their willingness to interact with them, as well as robots' Pragmatic, Hedonic and Attractive qualities. Through this investigation it was found that male seniors were more proactive than female ones in their attitude toward robots showing more willingness to interact with them and attributing more positive scores to robots' qualities. It was also observed that androids were clearly more preferred than humanoid robots no matter their gender. Finally, seniors' preferences were for female android robots with Asian traits and male android with Caucasian traits suggesting that both gender and ethnical features are intermingled in defining robot's appearance that generate seniors' acceptance.

The Internet of Things (IoT) makes attractive services available to smart objects and humans. To aim this, IoT devices need high sensing, reasoning, and real-time acting capabilities that can be also obtained by promoting adaptive forms of cooperation machine-to-machine among smart objects. The convergence of IoT and multiagent systems also relies on the association between software agents with IoT devices for exploiting their social attitude of interacting and cooperating for services. However, the choice of reliable partners for cooperation can be very difficult when IoT devices migrate across different environments, where the most part of their members will be unreferenced with respect to their trustworthiness. It is well known that agents reputation can be a viable aspect to consider to form social groups; therefore, a possible solution to this problem is to form groups of agents in each IoT environment, based on their social capabilities. In this respect, the first contribution of this paper is represented by a reputation model focused on building the reputation capital of each agent. Second, an algorithm capable to form groups of agents in IoT environments on the basis of their reputation capital was designed. Finally, since in this contest, it is important to spread reliable and certified information about the device/agent reputation in a distributed environment, the third contribution is represented by the adoption of the blockchain technology to certify the reputation capital. Some experiments we have performed show that the model is capable to detect almost all the misleading agents if their percentage is under a high enough threshold, and that good results in term of group composition are obtained. Moreover, the simulations show that, by adopting our model, malicious devices always pay for services significantly more than honest ones. We argue that the individual reputation capital of devices and, consequently, the overall reputation capital of the IoT community can take benefit from the adoption of the proposed approach.

Nowadays, a challenge for the “Internet of Things” (IoT) world is represented by the necessity of facing very complex and interactive tasks, such that IoT devices have to be equipped with hardware having very powerful capabilities. All this becomes particularly critical in presence of small and low-cost IoT devices. A way to deal with such problems is represented by the possible virtualization of the IoT environments over the cloud, the so called Cloud-of-Things (CoT), and then to associate each device with one or more software agents working in the Cloud environment. Moreover, the convergence of these technologies allows IoT devices to take significant benefits also by the social attitude of software agents to interact and cooperate. In this context, based on Machine-to-Machine (M2M) interactions, the choice of the partner for cooperating is a sensitive question, particularly in open and heterogeneous environments. If an agent does not hold suitable information to carry out a reliable choice then, similarly to real communities, it can ask information to other agents it considers as trustworthy. In this context, agents cooperation must be supported by a proper trust model which helps to select potential partners. This process can be further improved by partitioning the agents in different groups based on trust relationships. This way, each agent has the possibility to prefer the interactions with the agents belonging to its group that are, from its viewpoint, the most reliable for avoiding malicious behaviors and threats of different nature. To this purpose, we designed an algorithm, named CoTAG (CoT Agent Grouping algorithm), to form agent groups on the basis of information about reliability and reputation collected by the agents. To verify the efficiency and effectiveness of this algorithm, we carried out some experimentations in a simulated scenario. The results confirm the potential advantages deriving by the adoption of our proposal.

The need of managing complex and interactive activities is becoming a key challenge in the “Internet of Things” (IoT) and leads to request large hardware and power resources. A possibility of facing such a problem is represented by the possibility of virtualizing physical IoT environments over the so called Cloud-of-Things (CoT), where each device is associated with one or more software agents working in the Cloud on its behalf. In this open and heterogeneous context, IoT devices obtain significant advantages by the social cooperation of software agents, and the selection of the most trustworthy partners for cooperating becomes a crucial issue, making necessary to use a suitable trust model. The cooperation activity can be further improved by clustering agents in different groups on the basis of trust measures, allowing each agent will to interact with the agents belonging to its own group. To this purpose, we designed an algorithm to form agent groups on the basis of information about reliability and reputation collected by the agents. In order to validate both the efficiency and effectiveness of our approach, we performed some experiments in a simulated scenario, which showed significant advantages introduces by the use of the trust measures.

Internet of Things (IoT) is an emerging paradigm for cooperation among physical objects provided with computational and communication capabilities. The great amount of interactions occurring within the IoT environments expose IoT users and interconnected objects to security and privacy risks. Consequently, adding security and privacy into the IoT domain is fundamental for a wider IoT diffusion. In open and dynamic IoT environments such problems are more relevant for the greater possibility of anomalous behaviors and the adoption of approaches uniquely based on authentication methods could result infeasible and/or inadequate for creating an effective trustworthiness atmosphere. A possible solution, widely applied and investigated in different fields, is represented by trust- and reputation-based systems. To tackle the issues above we conceived a distributed reputation model along with a framework to manage information about IoT devices reputation. In particular, the model provides some countermeasures to detect malicious or cheating devices. To test the performance of our proposal, a set of experiments simulating the vehicular mobility on a basic urban network has been executed by providing promising results.

The success of inter-cloud VM migration depends on several different factors for which a good estimate should be made in advance by Cloud providers. In this context, the ability of the counterparts in performing related measures and appropriate evaluations will affect the success of the migration to be performed. We argue that the risks due to errors made in the evaluations above can be classified into two different classes: i) a direct (negative) consequence on the reputation built towards the client customer, and ii) additional costs due to waste of resources. In this work we propose a trust-based, multi-agent architecture on which software agents assist providers in taking decisions about VM migration, by performing an evaluation of the different risks discussed above. We envision a first class of software agents that computes trust information by means of a model that considers the critical concerns to take into account in advance for the VM migration process. A second class of software agents is employed to assist the decisional agents in per- forming measures about VMs and measuring errors in front of previous VMs migrations. Moreover, they are also employed to disseminate trust information by means of a gossip-based protocol, in order to provide fault tolerance, efficiency and redundancy.

A central research theme in the Online Social Network (OSN) scenario consists of predicting the trustworthiness a user should assign to the other OSN members. Past approaches to predict trust relied on global reputation models: they were based on feedbacks about the actions performed by the user in the past and provided for the entire OSN. These models have shown an evident limitation in considering the effects of malicious and fraudulent behaviors, thus making unreliable the feedbacks themselves. In this paper, we propose to integrate global reputation models with a local reputation, computed on the user ego-network. Some experiments, performed on real datasets shows that the global reputation is useful only if the size of the user ego-network is small, as for a newcomer. Besides, the integrated usage of global and local reputation leads to predict the expected trust with a very high level of precision.

Cloud/Grid systems are composed of nodes that individually manage local resources, and when a client request is submitted to the system, it is necessary to find the most suitable nodes to satisfy that request. In a competitive scenario, each node is in competition with each other to obtain the assignment of available Tasks. In such a situation, it is possible that a node, in order to obtain the Assignment of a task, can lie when declaring its own capability. Therefore, lying nodes will need to require the collaboration of other nodes to complete the task and consequently the problem arises of finding the most promising collaborators. In such a context, to make effective this selection, each node should have a trust model for accurately choosing its interlocutors. In this paper, a trust-based approach is proposed to make a node capable of finding the most reliable interlocutors. This approach, in order to avoid the exploration of the whole node space, exploits a P2P resource finding approach for clouds/grids, capable of determining the admissible region of nodes to be considered for the search of the interlocutors.

Several reliability-reputation models to support agents' decisions have been proposed in the past and many of them combine together reliability and reputation in a synthetic trust measure. In this context, we present a new trust model, called TRR, that considers, from a mathematical viewpoint, the interdependence between these two trust measures. This important feature of TRR is exploited to dynamically compute a parameter determining the importance of the reliability with respect to the reputation. Some experiments performed on the well-known ART platform show the advantages, in terms of effectiveness, introduced by the TRR approach.