Energy-Efficient Routing in Wireless Sensor Networks: A Comprehensive Review of Machine Learning, Optimization, Clustering, and Duty Cycling Techniques

Abstract

Wireless Sensor Networks (WSNs) are widely used in various applications, yet they remain limited by the energy constraints of sensor nodes. This review paper explores and compares recent advancements in energy-efficient routing strategies across four major categories: Q-Learning-based routing, traditional optimization algorithms, clustering-based protocols, and sleep scheduling techniques. Each approach is analyzed in terms of its methodology, simulation environment, performance metrics, and limitations. Q-Learning techniques provide adaptive and intelligent routing decisions but often lack real-world deployment. Traditional algorithms such as Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) offer reliable clustering but adapt poorly to dynamic environments. Clustering-based protocols, especially those integrating fuzzy logic or quantum methods, show strong results in simulations but assume static and homogeneous nodes. Sleep scheduling and duty cycling protocols significantly reduce idle energy waste, yet are rarely integrated with routing layers. The review identifies that most current protocols are evaluated only through simulations and face challenges such as congestion near sinks, lack of cross-layer integration, and scalability under heterogeneous conditions. Future research should focus on building lightweight, real-time learning frameworks that jointly optimize routing, clustering, and sleep scheduling in practical deployments.