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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.

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.

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.

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 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.

An important issue in Online Social Networks consists of the capability to generate useful recommendations 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 reputation, (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.

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.

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.

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.

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.

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.

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.

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 multi-cloud context. By this approach, cloud agents performances can be improved because they are substituted with agent clones that have shown a better behaviour.

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.

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.

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.