Navigation in the social internet-of-things (SIoT) for discovering the influential service-providers using distributed learning automata

Abstract

The integration of the social network concepts and IoT has led to the creation of a new concept called Social IoT, wherein objects or things can interact and provide services to one another. Efficient and distributed service navigation in such systems is essential due to the potentially huge number of services that are provided. This paper proposes a Distributed Learning Automata-based algorithm called DLA_N, which aims at finding service-providers (i.e., objects) with high popularity or influence. The main idea is that an object can use its friends or friends of its friends to search for the desired service provider. Taking into account the SIoT’s graph properties (i.e., the topology of the network), we define a new centrality metric that indicates the importance degree of a node in SIoT. Embedding a Learning Automata in each object, a distributed LA approach is proposed for the selection of the most influential nodes in the network. Starting from a requesting object, the proposed DLA_N algorithm learns to select a path containing objects with a high centrality metric. The distributed nature of our navigation results in high scalability and low navigation time. The results of performance evaluation indicate that the proposed method outperforms existing methods in the literature.