Energy Efficient Multiobjective Improved Wild Horse Optimization for Clustering and Routing in Wireless Sensor Networks

A Wireless Sensor Network (WSN) comprises nodes having small sensors and transceivers for sensing, tracking, and data collection in diverse scenarios.Managing sensor energy is crucial, especially in challenging environments where battery backup is difficult.To optimize sensor node energy, the integration of clustering and routing strategies becomes an effective solution.However, the deployment of sensor nodes introduces challenges such as high energy usage, packet loss, minimal network lifespan, and packet transmission delays.So, this research introduces an Energy Efficient Multiobjective Improved Wild Horse Optimization technique (EE-MIWHO) using Levy flight concept from Cuckoo Search Algorithm (CSA) to enhance WSN energy efficiency.The advantage of the Levy flight helps the WHO to improve its exploration ability for searching the optimal solutions effectively.The EE-MIWHO calculates an objective function, while CHs closer to the BS are involved in more transmission operations.This approach efficiently ensures a balanced distribution of energy consumption among network nodes.The primary objective of the proposed EE-MIWHO approach is reducing energy usage among WSN network, along with increasing the PDR, throughput, alive node count and network lifetime.To determine the enhanced performance of EE-MIWHO, comparative evaluations are conducted against the traditional approaches namely, Heterogeneous Optimized Cuckoo Krill (HECK) protocol, Crosslayer-based Opportunistic Routing Protocol (CORP), Low-Energy Adaptive Clustering Hierarchy with Improved Spider Monkey Optimization (ISMO), and Cross-layer-based Energy-Efficient WSN (CL-EEWSN).Thus, this research reveals that the EE-MIWHO outperforms the traditional approaches such as HECK and CORP with more count of alive nodes of 198 for 300 rounds.