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Artificial Neural Networks (NNs) have been recognized as a powerful tool for automatically learning complex relationships in data. In this paper, we propose to apply such a tool for modeling forest regeneration, a possibility not yet investigated in the literature. In order to evaluate the capability of NNs to simulate initial recruitment of pine species in Mediterranean forests, a feed-forward multi-layer neural network has been applied to seed germination and seedling survival of four pine species under three soil conditions, with or without seed protection, in Castilla La Mancha (Central-Eastern Spain). The experimental campaign has shown good performance in predicting the two pine initial recruitment stages. The proposed approach may help to predict the success of natural regeneration in Mediterranean pine forests under different basal areas and management strategies.

The Internet of Things (IoT) promises to change many aspects of our daily lives thanks to the opportunity of interconnecting a massive numbers of smart objects with increasing computational, storage, communication, and power capabilities, in such a way making “smart” and “interactive” most of the world around us. In other words, smart objects and humans will be involved together in pervasive, proactive, and collaborative activities to orchestrate and execute increasingly complex and sophisticated tasks. In such a scenario, thanks also to a greater ecological awareness of people, a promising field of application for IoT technology is the monitoring of natural habitats in automatic or semiautomatic mode. A potentially effective and efficient solution is to form composite teams joining human operators and IoT devices. In such teams, not only the kind of team members will be different, i.e., humans and IoT devices, but also the IoT devices will be heterogeneous among them in terms of characteristics and performance. However, a basic requirement for a good team is the existence of high levels of mutual trust among its members. In other words, for the formation of a good team, it becomes of primary relevance to know and adequately represent the trustworthiness of the individual team members. To this end, our contribution can be summarized as (i) introducing a trust measure that takes into account both the reputation of devices and the accuracy of their measures; (ii) designing a framework that based on the proposed trust measure forms temporary teams for environmental ecosystems made of humans and IoT devices; and (iii) testing the proposed framework by simulating a collaborative environmental monitoring activity. The simulation results confirmed the advantages of the proposed approach in terms of performance and appreciation of the composite temporary teams that have been formed in this way.

The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs' reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.

In this work we introduce a clustering reputation-based approach for edge-based IoT (Internet of Things) environments where it’s possible to process and save data on edge servers. In particular, once in an edge domain two Smart IoT Objects (SOs) exchanged a service, supported by their associated agents, they will release a feedback about their respective counterparts to the edge server managing their current domain to update the counterparts’ reputation score. However, even though in the proposed systems the reputation is managed and hosted in the Edge, the SOs’ reputation scores are also periodically saved on the Cloud. In this way, when a SO moves itself from an edge domain to another edge domain its reputation score will be available on the Cloud. Moreover, each edge server provides to cluster its managed SOs on the basis of their reputation scores to strengthen the competitiveness of SOs into the domain. This paper describes the proposed distributed clustering reputation-based framework for heterogeneous SOs and its performance in a simulated scenario.

Few experiences of Artificial Neural Networks (ANNs) for hydro-logical predictions in forest soils after wildfire and post-fire treatments are available in literature. To fill this gap, an ANN model has been adapted to predict surface runoff and soil erosion in Mediterranean burned pine forests (Central Spain), and tested against hydro-logical observations at plot scale throughout 2 years. The model gave very accurate runoff and erosion predictions in burned and non-burned soils as well as for all soil treatments (mulching and/or logging or not). Although further experimental tests are needed to validate the ANN applicability to soils in burned and treated forests in other ecosystems, the use of ANN may be useful for landscape planners as decision support system for the integrated assessment and management of forests.

In the Internet of Things, smart objects can build multidimensional and context-sensitive network infrastructures potentially rich of social interactions. Smart objects can be associated with software agents to boost social interactions and realizing complex and sophisticated forms of collaboration of objects with both other objects and people. In such a scenario, there exists the possibility to interact with unreliable partners exposing agents to the risks deriving by malicious behaviors. To mitigate these risks, Trust and Reputation Systems can be adopted to provide each agent with appropriate trustworthiness measures about the potential counterparts in order to select the best ones. In this context, our contribution consists of (i) a method to preliminarily identify the best candidates as malicious in order to consider them as pre-untrusted entities and (ii) a novel effective reputation model able to detect collusive malicious agents without introducing collateral effects with respect to the reputation scores of honest agents.

The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning. In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.

In competitive Multi-Agent Systems (MASs) for IoT, an important task is to form friendships and groups for mutual cooperation. The existing proposals try to improve the profit of individual agent or group but, on the other hand, the most aggressive agents may take advantages from these approaches by means of malicious behaviors. In this work we model a MAS framework of non cooperative games by taking into account i) the reliability of agents relationships through a trust model and ii) the community ability to furnish a good environment to its members through its social capital. Thus, we designed an algorithm that maximizes the social capital. We emphasize two main findings: i) the result is a Nash equilibrium and ii) the agents are awarded only if they behave correctly.

In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion should be solved since the participants may not be trusted. We propose a consistent and trust fusion method with the consortium chain to reach a consensus, and complete the self-organized trusted decentralized collaborated learning. In each consensus process, consensus candidates check others’ trust levels to ensure that they tends to fuse consensus with users with high trust, where the trust levels are evaluated by scores according to their historical behaviors in the past consensus process and stored in the public ledger of blockchain. A trust rewards and punishments method is designed to realize trust incentive consensus, the candidates with higher trust levels have more rights and reputation in the consensus. Simulation results and security analysis show that the method can effectively defend malicious users and data, improve the trust perception performance of the whole federated learning network, and make the federated learning more trusted and stable.

Smart Workshops are experiencing the need of a mobile intelligence for mining both learning patterns and knowledge from the wide sea of data generated by both mobile users and mobile technologies. Indeed, mobile intelligence would represent the ideal substratum for providing "agentified" robots with a plethora of advanced capabilities (e.g., visual recognition, fault detection, self-recovery) and, hence, with high-level functionalities, like production line control, asset movement, connectivity restore. Besides the operational plane, however, mobile intelligence can be successfully exploited also in organizational tasks, like the formation of temporary, ad-hoc teams for accomplishing a given target. The complexity of some industrial operations, indeed, often demands the involvement of several, heterogeneous group of robots and the adequate representation of the reciprocal trustworthiness represents a key pre-requisite. It holds particularly for the Automated Guided Vehicles (AGVs) which are increasingly involved in collaborative activities aimed to optimise storage, picking, and transport functions in a wide variety of workshop areas. Therefore, in this paper we define a trustworthiness model for agentified AGVs based on the mix of their reputation and reliability and we present an agent-based framework implementing the related team formation strategy. The improvements obtained in terms of effectiveness and efficiency from the AGV team are observed and measured through a simulation activity, in which realistic settings for an industrial applications have been considered.

IoT devices dealing with complex tasks usually require powerful hardware capabilities or, as a possible alternative, to get on the Cloud those resources they need. When an IoT device is 'virtualized' on the Cloud, it can take benefit from relying on one or more software agents and their social skills to mutually interact and cooperate. In particular, in a Cloud of Things scenario, where agents cooperate to perform complex tasks, the choice of a partner is a sensitive question. In such a context, when an agent is not capable to perform a reliable choice then, like real social communities, it can ask information to other agents it considers as trustworthy. In order to support agents in their partner choices, we conceived a local trust model, based on reliability and reputation measures coming from its ego-network, adopted to partition the agents in groups by exploiting trust relationships to allow agents to be associated with the most reliable partners. To this aim, we designed an algorithm to form agent groups by exploiting available local trust measures and the results obtained in a simulated scenario confirmed the potential advantages of this approach.

In the Cloud of Things, data are stored and processed in the cloud and the results are sent to IoT Smart Objects (SOs) with, generally, a network latency overhead due to the distances between the cloud and the local IoT networks. Moreover, trusting inappropriate counterparts can expose SOs to threats and to mitigate them we propose a clustering reputation-based approach for IoT Edge-based platform processing and storing data on the “edge”, nearby the SOs. Whenever SOs interact for services, a feedback is sent to an Edge server to calculate their reputation scores. In this way, the reputation systems is moved from the cloud to the edge servers, but available on the cloud if a SO will change edge domain. To this aim, we designed a distributed Trusted Object Framework (TOF) where heterogeneous OSs host and exploit the assistance of associated software agents and verified its performance in a simulated scenario which confirmed the potential advantages of TOF.

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.

An important issue in Online Social Networks consists of the capability to generate useful recommen- dations for users, as peers to contact in order to establish friendships and collaborations, interesting resources to use and so on. This implies the necessity of evaluating the trustworthiness a user should assign to other members of his/her online community. In the past literature, a common approach for predicting trust is represented by a number of models that rely on “global” reputation: they are based on the evaluation of the behaviors of the users, that is shared across the entire community. These models, however, show an evident limitation due to the difficulty of taking the effects of malicious or fraudulent behaviors into account, thus making the feedback themselves. Other approaches, that consider also a local perspective of the trust, are limited by the fact they are supervised, i.e. they need a training phase in generating recommendations. In this paper, we propose a novel approach to extend global reputation models with a local reputation, computed on the ego-network of the user, by means of an unsupervised approach. We characterize our model by considering (i) the different relevance given to local and global repu- tation, (ii) the threshold that is used to consider a user unreliable and (iii) the dimension of the user’s ego-network. Experiments performed on a real data set show that global reputation is useful only if the size of the user ego-network is small, as in the case of a newcomer. Moreover, the combined usage of global and local reputation leads to predict the expected trust with a high level of precision.

Accurate predictions of surface runoff and soil erosion after wildfire help land managers adopt the most suitable actions to mitigate post-fire land degradation and rehabilitation planning. The use of the Artificial Neural Networks (ANNs) is advisable as hydrological prediction tool, given their lower requirement of input information compared to the traditional hydrological models. This study proposes an ANN model, purposely prepared for forest areas of the semi-arid Mediterranean environments. The ANN hydrological prediction capability in non-burned, burned by wildfire, and burned and then treated soils has been verified at the plot scale in pine forests of South-Eastern Spain. Runoff and soil loss were much higher than non-burned soils (assumed as control), but mulch application was effective to control runoff and soil erosion in burned plots. Moreover, logging did not affect the hydrological response of these soils. The model gave very accurate runoff and erosion predictions in burned and non-burned soils as well as for all soil treatments (mulching and/or logging or not), with only one exception (that is, in the condition with the combination of treatments which gave the worst performance, burning, mulching and logging), as shown by the exceptionally high model efficiency and coefficients of determination. Although further experimental tests are needed to validate the ANN applicability to the burned forests of the semi-arid conditions and other ecosystems, the use of ANN can be suggested to landscape planners as decision support system for the integrated assessment and management of forests.

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.

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.

Matching users profile in virtual communities can represent the most natural way of representing group homogeneity, i.e. how much the group members are mutually linked. However, optimizing profile matching does not guarantee the group cohesion, i.e. that the group will continue to be homogeneous in time. Moreover, computing profile matching in large virtual communities can be very expensive, and cannot be integrated in a fully distributed system. In the past, we have demonstrated that using users mutual trust, along with profile matching, can help to improve groups homogeneity. In this work we demonstrate, by an extended set of experiments on datasets extracted from real communities, that trust measures can effectively replace profile matching in order to optimize group's cohesion. A further interesting result is represented by the fact that it is also possible to replace the global trust measure with a local measure of trust, called local reputation, which is not highly sensitive to the size of the network, thus allowing to perform computations which are limited on the size of the ego-network of the single node.

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising efficiency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

Observing the features of the information actually stored in the Web, we can recognize that an important issue to be investigated is that of discovering relationships between groups of objects. In particular, a great interest is emerging on finding groups of objects mutually linked by reciprocal relationships of trustworthiness. In this paper, we propose a model to represent the case of trust-based groups of objects, and we present an algorithm for detecting trust associations in virtual communities in presence of these groups. Such an algorithm consists in determining particular sub-structures of the community, called trust groups, representing objects mutually connected by strong trust relationships. We technically formalize our idea and algorithm, and we present a complete example of how our approach works.

Members of virtual communities generally expect that their groups satisfy some given requirements. For this aim, the profile matching between user requirements and group characteristics can be considered as the most natural way to represent the group homogeneity, measuring how much the group members are mutually linked. However, optimizing profile matching does not guarantee that the group will continue to be homogeneous in time (i.e., cohesive). In the past we have already shown that, when group formation is driven by trust measures and profile matching group homogeneity is improved. In this work, we prove by experiments on a dataset extracted from a real social network, that trust measures can be used to effectively replace profile matching for optimizing group’s cohesion. Furthermore, we prove also that using a local trust measure will does not penalize the cohesion of the group.

IoT devices dealing with complex tasks require powerful hardware capabilities or to get resources on the cloud. When an IoT device is “virtualized” on the Cloud, it can rely on one or more software agent that can exploit its social attitude to interact and cooperate. In this context, the choice of a partner to cooperate is a sensitive question but when an agent cannot perform a reliable choice then, like real communities, it can ask information to other agents it considers as trustworthy. This process can be improved by partitioning the agents in groups by using trust relationships to allow agents to interact with the most reliable partners. To this aim, we designed an algorithm to form agent groups based on reliability and reputation information and the results of some simulations confirmed its potential advantages.

In this paper we consider a CoT (Cloud of Things) scenario where agents cooperate to perform complex tasks. Agents have to select reliable partners and, in some cases, they don't have enough information about their peers. In order to support agents in their choice and to maximize the benefits during their cooperation, we combined several contributions. First of all, we designed a trust model which exploits the recommendations coming from the ego networks of the agents. Secondly, we propose to partition the agents in groups by exploiting trust relationships to allow agents to interact with the most reliable partners. To this aim, we designed an algorithm named DAGA (Distributed Agent Grouping Algorithm) to form agent groups by exploiting available reliability and reputation and the results obtained in a simulated scenario confirmed its potential advantages.

critical issue for small and low-cost Internet of Things (IoT) devices facing multiple complex, advanced and interactive tasks trying to save their power resources. To reach these goals IoT devicescan use the capabilities of nearby devices having suitable resources, given that they make their resources available for free or with a determinedcost. In such a context, IoT devices can take significant benefits by exploiting the social attitude of software agents to mutually interact and cooperate with other agents they consider as trustworthy. However, in wide communities it is common that a lot of members are unreferenced with respect to the own trustworthiness and, therefore, the task of carrying out a reliable choice about a potential partner can be very difficult. To tackle such an issue, we propose an agent framework where each IoTdevice is associated with an agent that helps its device in choosing reliable partners for its tasks. To this aim, we designed a reputation modelimplementing some countermeasures against malicious IoT devices. Toverify the efficiency and effctiveness of our proposal, we carried out some experiments in a simulated scenario, which confirmed the potential advantages deriving by its adoption.

In this work we present an approach aimed at maximizing the global QoS perceived within a large-scale federation of computing infrastructures. This approach exploits the combina- tion of ( i ) a trust model for a network of software agents, designed to assist federated computing nodes, and ( ii ) a decentralized procedure which leads to the formation of coali- tions between them. The proposed solution is based on a generic SLA-based federated ar- chitecture and the concept of “Global Capital”which reflects the global QoS offered by the federation. Finally, a number of experimental trials prove that, by means of the proposed approach, the Global Capital improves.

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising effciency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

Improvements in computational and network capabilities of mobile devices along with the wide spread of wireless networks for smart cities, allow users to make accessible temporarily unused communication resources for free or for a fee. Such interesting scenario introduces, however, some critical issues due to reliability of users sharing/consuming resources, which are a further concern with respect to those, strictly referred to the security. To tackle such problems, in this paper we propose a distributed multi-agent framework, exploiting reputation information, which considers Both the price paid by mobile users for resources and some countermeasures to detect malicious users, as confirmed by experimental results.

Thematic groups are gaining a lot of attention and high centrality in OSNs, as users share opinions and/or mutually collaborate for reaching their targets. Since users can be affiliated with groups belonging to different social networks, they can be supported by personal software agents able to perform activities aimed at supporting relationships and mutual cooperation among heterogeneous OSNs thematic groups. Basically software agents can encode users profiles with detailed information to be related with specific groups. This work discusses a multi-agent framework whose structure focuses on the role of software agents and the use of a common, shared dictionary for each group. Personal software agents are associated to OSN users to share knowledge for a specific theme for a number of groups related to that topic. Group agents are defined to support each group on each OSN by interacting with personal agents to manage group affiliation and enrich the common dictionaries of their own groups. The common dictionary of the group is a key element to provide knowledge sharing and interoperability between personal and group agents. In the proposed approach each user agent is able to personalize its own dictionary and enrich that of its own groups by means of selected categories.

Learning software agents are able to assist Cloud providers in taking decisions about resource management at any level, as they are able to collect knowledge and improve their performances over time by means of learning strategies. On the other hand Cloud Federations allow providers to share computational infrastructures in order to build a distributed, interoperable multi-cloud context. In this work we present an evolutionary approach based on agent cloning, i.e. a mechanism of agent reproduction allowing providers to substitute an “unsatisfactory” agent acting in a “cloud context” with a clone of an existing agent having a suitable knowledge and a good reputation in the multicloud context. By this approach, cloud agents performances can be improved because they are substituted with agent clones that have shown a better behavior.

Cloud Computing is a versatile computing paradigm capable of attracting a wide variety of applications. However, the necessity to provide a wide range of complex services led providers to establish mutual agreements to provide large-scale distributed multi-cloud environments. Providers gain the opportunity to compose service workflows that are effective and efficient, taking resources of their own competitors, and the capability to satisfy unexpected workload peaks. In this paper, we propose a reputation-based model aiming at supporting the service composition by considering measures of QoS collected by the measuring systems, and reputation measures collected with the customers by means of users feedback.

In this work, we propose an algorithm, called E-Learning Ontology Enrichment (ELOE), to derive a global representation from the personal ontologies of different agents present in an e-Learning MAS. Using ELOE, each agent of a MAS-based e-Learning system can autonomously enrich his own ontology by using semantic negotiation and, at the same time, it can access to the global ontology to have a view of the terms used by all the other agents. Each term of the global ontology is associated with a set of meanings, and each meaning is associated with the set of agents that have used it. This way an agent, desiring to send a message to an interlocutor, is able of choosing from the global ontology the most suitable term with the most appropriate meaning. Only if the agent does not find in the global ontology the appropriate term, he will use a personal term that probably will lead to a new semantic negotiation process. This way, the use of the onerous task of the semantic negotiation will be limited to only the strictly necessary situations, and consequently the whole communication cost is decreased.

In this work we deal with the issue of optimizing the global Quality of Service (QoS) of a Grid Federation by means of an aggregation model specifically designed for intelligent agents assisting Grid nodes. The proposed model relies on an algorithm, called FGF (Friendship and Group Formation), by which the nodes select their partners with the aim of maximizing the QoS they perceive when a computational task requires the collaboration of several Grid nodes. In the proposed solution, in order to assist the selection of the partners, a suitable trust model has been designed. Since jobs sent to Grid Federations hold complex requirements involving well defined resource sets, trust values are calculated for specific sets of resources. We also provide a theoretical foundation and some experiments to prove that, by means of the adoption of the FGF algorithm suitably supported by the proposed trust model, the Grid Capital (which respect the global QoS) of the Grid Federation is eventually improved.

Online Social Networks are suitable environments for e-Learning for several reasons. First of all,there are many similarities between social network groups and classrooms. Furthermore,trust relationships taking place within groups can be exploited to give to the users the needed motivations to be engaged in classroom activities. In this paper we exploit information about users’ skills,interactions and trust relationships,which are supposed to be available on Online Social Networks,to design a model for managing formation and evolution of e-Learning classes and providing suggestions to a user about the best class to join with and to the class itself about the best students to accept. The proposed approach is validated by a simulation which proves the convergence of the distributed algorithm discussed in this paper.

Highly scalable, resilient commodity hardware, along with the availability of huge amounts of data, allows consumers and companies to access to a wide range of high-value services through a pay-per-use, pay-as-you-go model. The increasing need for intelligent services at large-scale is bringing providers to optimise the use of resources in order to meet the increasing web-scale demand. Programming models are evolving to address the high complexity of service composition, which need resiliency and efficiency. Algorithms and technologies for intelligent orchestration allow multiple owners of cloud and grid infrastructures to satisfy customer requirements and business constraints. This paper aims to assist the development of new architectures, frameworks, middlewares and computing models, as well as innovative applications, aimed at supporting scientists and business analysts in the development of unified computing frameworks and data analysis at large scale. This paper summarises a number of contributions given in the area of distributed cloud and grid computing, with particular emphasis on service orchestrations and emergent applications for high-value services.