Automated Service Search Model for the Social Internet of Things

Abstract

The social internet of things (SIoT) is one of the emerging paradigms that was proposed to solve the problems of network service discovery, navigability, and service composition. The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT, an object can offer multiple services and different objects can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service customization and service filtering. This problem is known as service explosion. The selection of suitable service that fits the requirements of applications and objects is a challenging task. To address these issues, we propose an efficient automated query-based service search model based on the local network navigability concept for the SIoT. In the proposed model, objects can use information from their friends or friends of their friends while searching for the desired services, rather than exploring a global network. We employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality scores. The distributed nature of our navigation model results in high scalability and short navigation times. We verified the efficacy of our model on a real-world SIoT-related dataset. The experimental results confirm the validity of our model in terms of scalability, navigability, and the desired objects that provide services are determined quickly via the shortest path, which in return improves the service search process in the SIoT.