Using trust and local reputation for group formation in the Cloud of Things

Nowadays, a challenge for the "Internet of Things" (loT) world is represented by the necessity of facing very complex and interactive tasks, such that loT devices have to be equipped with hardware having very powerful capabilities. All this becomes particularly critical in presence of small and low-cost loT devices. A way to deal with such problems is represented by the possible virtualization of the loT 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 loT 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. (C) 2018 Elsevier B.V. All rights reserved.