Community detection in the social internet of things based on movement, preference and social similarity

Abstract

Internet of Things (IoT) is one paradigm many visions technology. One of the many visions of Internet of Things is to make Things sociable. This is achieved by integrating IoT and Social networking which may lead to a new paradigm called Social Internet of Things (SIoT). SIoT is defined as collection of intelligent objects that can autonomously interact with its peers via owners. In a SIoT scenario, detecting and characterizing a network structure is very important. In this paper, we propose a new community detection algorithm that detects communities in SIoT using three metrics namely social similarity, preference similarity and movement similarity. To the best of our knowledge this is the first work that detects communities in large scale Social Internet of Things using social, preference and movement similarity. The experimental results show that the proposed community detection scheme achieves higher quality results in terms of detection rate and execution time when compared to existing methods.