Improved Energy Optimization Scheme in Wireless Sensor Networks Using Residue Number System and Ant Colony Optimization Approach

Abstract

Wireless Sensor Networks (WSNs) are crucial for environmental monitoring, healthcare, agricultural, and industrial automation; however, their energy efficiency remains a significant challenge. Although the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol improves energy conservation through clustering, it suffers from unbalanced cluster head (CH) distribution and suboptimal routing paths, leading to premature network degradation. To address these limitations, this study proposes CRT-LEACH-ACO, a novel hybrid protocol that integrates: The Chinese Remainder Theorem (CRT) for deterministic and load-balanced clustering and Ant Colony Optimization (ACO) for adaptive, energy-efficient routing. This study evaluates four protocols (LEACH, CRT-LEACH, LEACH-ACO, and CRT LEACH-ACO) through Python-based simulations, analyzing network lifetime, energy consumption, throughput, and Packet Delivery Ratio (PDR). The results of this study demonstrate that CRT-LEACH-ACO outperforms conventional LEACH in all metrics, while also surpassing its variants (CRT-LEACH and LEACH-ACO). The proposed protocol provides a scalable and robust solution for energy-constrained WSNs, making it suitable for long duration monitoring applications. This work advances WSN protocol design by combining mathematical optimization (CRT) with bio-inspired routing.