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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.

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.

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.

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.

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.

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.

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.