Optimised rotating energy-efficient clustering for wireless sensor devices by sewing training-based optimisation

Abstract

Wireless sensor networks (WSNs) have become well-known as innovative, active, and robust technology accepted in many real-world applications. Due to the power supply restrictions and the power limitations of sensors that are typically known in WSN, using energy becomes a challenge in networks. These two restrictions are essential for achieving energy efficiency and raising the network’s lifetime in WSN. Clustering develops multipath routing and scalability, performing optimisation, and making WSN naturally more reliable. This paper introduces an Optimised Rotating Energy Efficient Clustering for Heterogeneous Devices (OREECHD). OREECHD is a clustering technique for heterogeneous WSNs that presents a unique cluster head selection method based on node residual energy and node-induced work. OREECHD defines the term intra-traffic rate limit (ITRL). The document outlines communication restrictions for traffic inside a network with WSNs. ITRL could be applied to develop energy efficiency. We apply the Sewing Training-Based Optimization (STBO) algorithm to recognise the best ITRL in various WSN adjustments. The simulation results show that the proposed algorithm using clustering based on the best ITRL improves the energy consumption in the sensor network by 8.9 % over the REECHD. The simulation outcomes account for the number of dead nodes present in the OREECHD and REECHD networks during the 1 400 and 1 250 rounds, respectively. The network lifetime is significantly improved compared to REECHD, since OREECHD is a classic example of an unequal clustering algorithm. The network’s lifetime is 1 200 rounds, which exceeds the REECHD lifetime of 800 rounds. The rate of residual energy at the average node decreases from 19.39 % to 15.41 %.