Optimizing Energy Efficiency in Wireless Sensor Networks via Cluster-Based Routing and a Hybrid Optimization Approach

Abstract

Wireless Sensor Networks (WSNs) are increasingly deployed to survey various environmental conditions, finding applications across domains such as agriculture, healthcare, and environmental monitoring. The sensors within these networks are tasked with collecting data and transmitting it to a central sink node through wireless means. Given that the sensor nodes operate on battery power and are often situated in remote locations where maintenance poses logistical challenges, energy conservation emerges as a critical issue. This study introduces an Energy-based Multiobjective Hybrid Optimization Algorithm (E-MHOA), designed to optimize cluster-based routing protocols to enhance energy efficiency in WSNs. The proposed E-MHOA integrates the Cuckoo Search Algorithm (CSA) with the Whale Optimization Algorithm (WOA) to judiciously select Cluster Heads based on their residual energy levels. The primary focus of the E-MHOA is to facilitate improved energy efficiency and data delivery within the context of agricultural monitoring applications. An array of performance metrics, including energy efficiency, End-to-End Delay (EED), packet drop, and network throughput, were employed to evaluate the efficacy of the E-MHOA. Comparative analyses were conducted against existing methodologies, such as MWCSGA, PAwCOR, and CEELBRP. The results of the simulation show that the E-MHOA approach performs noticeably better than the MWCSGA in terms of energy efficiency, achieving a notable efficiency rate of 98.09% in networks comprising 100 nodes.