An efficient social-like semantic-aware service discovery mechanism for large-scale Internet of Things

Abstract

Due to the enormous search space, dynamic availability, and restrictions on geographic positions, achieving a scalable and efficient service discovery mechanism for large-scale Internet of Things (i.e., IoT) is a challenging job. Owing to the similarity between social networks and IoT, social strategies can be integrated to improve the performance of IoT solutions. In this paper, we propose an efficient social-like semantic-aware service discovery mechanism named SLSA by mimicking human-like social behaviors and exploring cooperative intelligence. Our mechanism can discover desired services in a fast and scalable manner. The update process of knowledge index adopts a dual-modular-ordering stack strategy that makes search more efficient. Considering the semantic similarity and semantic relativity of two concepts in the domain ontology, we introduce the fuzzy logic method to calculate their correlation degree for device ranking. The SLSA implements an adaptive forwarding strategy, where the service query is forwarded to a selected subset of neighboring devices in a preferred order. We conduct comprehensive experiments to evaluate four mechanisms by establishing dynamic environments. The simulation results show that the SLSA achieves better performance than the other relevant mechanisms with three aspects. Furthermore, confirmative tests are carried out on the characteristics of small-world networks.