ENERGY EFFICIENT ROUTING ALGORITHMS FOR WIRELESS SENSOR NETWORKS IN HARSH ENVIRONMENTS

Abstract

Purpose This study investigates the design and performance of energy-efficient routing algorithms tailored for Wireless Sensor Networks (WSNs) deployed in harsh environments. These environments include high-temperature zones, areas with electromagnetic interference, or remote and hostile terrains where node failure is frequent and energy resources are constrained.

Methodology The research synthesizes recent advancements in adaptive routing protocols that dynamically adjust based on environmental feedback. Both heuristic-based and machine learning-enhanced approaches are examined through simulation using a custom-built network model mimicking real-world deployment conditions in harsh environments.

Findings Energy-aware clustering protocols outperformed traditional flat routing schemes in prolonging network lifetime. Machine learning-based protocols demonstrated superior adaptability to environmental variability but incurred higher initial energy costs. The balance between adaptability and efficiency is crucial for optimal protocol performance.

Practical implications These findings provide critical insights for engineers and researchers developing WSNs for use in critical applications such as disaster zones, battlefield surveillance, and remote environmental monitoring, where network longevity and robustness are paramount.

Originality Unlike works, this study explicitly models environmental stressors and node unreliability, contributing an empirically validated framework for comparing routing strategies under realistic harsh conditions.