Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are utilised in a variety of applications due to their capacity to capture and transmit environmental data. Clustering has emerged as an efficient method for improving energy efficiency in WSNs. To resolve these issues, we propose an Adaptive Wind Driven Optimisation based Energy Aware Clustering Scheme (AWDO-EACS) for WSNs. The AWDO-EACS model presents an extended form of the Wind Driven Optimisation (WDO) algorithm, designated AWDO, with optimised inherent term values. The proposed model takes into account multiple objectives, such as energy consumption, distance, and end-to-end latency, in order to achieve superior energy efficiency and an extended network lifetime. To validate the efficacy of the AWDO-EACS model, extensive experiments with varying node counts were carried out. In terms of network stability, energy efficiency, end-to-end latency, packet delivery ratio, throughput, packet loss rate, and network lifetime, the results demonstrate that the AWDO-EACS outperforms contemporary clustering strategies. Specifically, the AWDO-EACS obtained a significant increase in energy efficiency, with a 27.35 percent improvement over existing clustering techniques for 20 nodes and an 83.41 percent improvement for 100 nodes. In addition, the end-to-end latency was considerably reduced, with a 96-round lifetime for 20 nodes and a 74-round lifetime for 100 nodes, compared to 37 and 20 rounds, respectively, for GA-LEACH and MW-LEACH. In addition, the AWDO-EACS demonstrated superior packet delivery performance, with a 99.32% delivery ratio for 100 nodes, eclipsing the 76.90% and 82.65% of GA-LEACH and MW-LEACH, respectively. Moreover, the AWDO-EACS model demonstrated a remarkably low packet loss rate of 0.68 percent for 100 nodes, compared to 23.10 percent for GA-LEACH and 17.35 percent for MW-LEACH. The effectiveness of the proposed AWDO-EACS model in enhancing the overall performance of WSNs is demonstrated.

Keywords

• clustering
• energy efficiency
• metaheuristics
• network stability
• objective function
• WSN