SENET: A novel architecture for IoT-based body sensor networks

Abstract

Abstract Wireless sensor networks (WSNs) have been applied to various fields of study including medicine, agriculture, and engineering. Although recently, many architecture styles have been proposed to manage WSNs, most of them have ignored the application of artificial intelligence (AI) in wireless body sensor networks (WBSN). To this end, the present study aims to introduce a novel architecture (SENET), which is based on AI techniques and consists of three main layers. After describing the proposed architecture, the performance of four efficient and popular algorithms, i.e., world competitive contests (WCC), particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) is investigated for covering WBSNs using k head clusters (the k-coverage problem). The results show not only that the proposed architecture saves energy consumed by the wireless sensors, but also that the WCC algorithm is a suitable option for determining the positions of sensors in the proposed architecture in terms of WSN energy-consumption, the total number of required sensors, and reliability. The results also show that the proposed WCC algorithm, with an average 38.44 value of score on nine scenarios, outperforms other techniques.