EHOAER: An Improved Hybrid ALO-GA Framework for Optimized Routing in Heterogeneous Sensor Networks

Abstract

Energy-efficient routing is important in wireless sensor networks (WSNs) since sensor nodes are energy-constrained. This paper proposes an Enhanced Hybrid Optimization Approach for Energy-efficient Routing (EHOAER), an advanced version of the Hybrid Ant Lion Optimization-Genetic Algorithm (ALO-GA) framework. EHOAER uses dual optimizations: cluster θ values for balanced node distribution and Cluster Head (CH) eligibility distance for refined CH selection. CH candidates are ranked based on residual energy and proximity to the cluster center, with an adaptive fallback strategy to ensure reliable selection. The protocol is tested on both homogeneous and heterogeneous networks, enhancing its applicability across varied scenarios. Using hierarchical data transmission, EHOAER enables member nodes to transmit data to their CHs for aggregation and forwarding to the base station, significantly reducing energy consumption. Simulation results clearly show better energy efficiency and network lifetime compared to the present methods, but better performance in terms of data delivery. Practical uses include monitoring wildlife and fires in the forests and crops and soils in agriculture to show versatility in resource-poor environments.