Hybrid Optimization Approach for Energy-efficient Routing (HOAER) in Wireless Sensor Networks: A Case for Forest and Agricultural Monitoring

Abstract

In wireless sensor networks (WSNs), the design of energy-efficient routing protocols is critical due to the constrained energy resources of sensor nodes. To address this challenge, we propose the Hybrid Optimization Approach for Energy-efficient Routing (HOAER), which leverages a hybrid Ant Lion Optimization-Genetic Algorithm (ALO-GA) framework to optimize cluster formation. For a given number of clusters NNN, the ALO-GA optimization generates NNN distinct θ\thetaθ values to ensure a balanced distribution of nodes across all clusters. HOAER introduces a Cluster Head (CH) Eligibility Rule to enhance energy efficiency. Eligible nodes within a defined distance ddd from the cluster center are considered for CH selection, with the node possessing the highest residual energy chosen as the CH. If no eligible nodes are found, a fallback mechanism selects the node closest to the base station as the CH. The protocol employs a hierarchical data routing mechanism, where member nodes transmit data to their CH, which aggregates and forwards it to the base station. This integration of ALO-GA optimization with hierarchical routing significantly minimizes energy dissipation and prolongs the network's operational lifespan. Simulation results demonstrate that HOAER outperforms existing state-of-the-art protocols in terms of energy efficiency, network longevity, and data delivery. Moreover, HOAER's applicability extends to real-world scenarios such as forest and agricultural monitoring. For instance, the protocol can be deployed in forests for tracking wildlife movements and predicting early fire alarms, or in agricultural fields for monitoring soil conditions and crop health, offering practical utility in resource-constrained environments.