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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.

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.

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.

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.