An Improved Evolutionary Method for Social Internet of Things Service Provisioning Based on Community Detection

Abstract

Social IoT (SIoT) refers to socializing in the Internet of Things (IoT), where things generate social relationships. Due to the development of objects and issues such as delayed response, slow search, and composite service process, distributed object service discovery, selection, and composition based on the social structure have become essential challenges in the SIoT. Therefore, it is necessary to provide an efficient method for evaluating the effectiveness of service discovery in identifying suitable devices to offer requested services and the best composition strategy for combining requested services. This paper presents a new community detection algorithm that detects IoT devices with social connections in the SIoT network to facilitate service discovery and composition by reducing search space. Additionally, it introduced a new service provisioning algorithm to optimize service discovery and composition, called An Improved Genetic Algorithm based on Community Detection (IGA-CD). Its effectiveness in detected communities is better than other methods in computation modularity, execution time, and cluster assignment quality methods by determining the network’s ideal devices. Experimental results demonstrate the efficacy of the proposed algorithms, which outperform other approaches in terms of scalability, efficiency, and flexibility. The IGA-CD average execution time is 0.129 seconds, which proves its efficiency and faster composition.

Keywords

• Social Internet of Things
• service discovery
• service composition
• community detection
• improved genetic algorithm