A Comprehensive Review of Energy-Efficient Routing Protocols and Optimization Frameworks in Wireless Sensor Networks

Abstract

Energy efficiency has emerged as the central design requirement in Wireless Sensor Networks (WSNs), particularly due to the limited battery resources and the impracticality of physical maintenance in harsh deployment regions. This review synthesizes ten contemporary studies on energy-efficient routing, spanning swarm intelligence, fuzzy-logic–assisted multi-criteria clustering, Pareto-optimal evolutionary strategies, multipath routing with load balancing, LEACH-based enhancements, and centralized cluster management for mobile-node environments. Contributions such as Whale Swarm-based routing, collaborative energy-efficient routing for emerging 5G/6G WSNs, evolutionary architecture reviews, Coyote Optimization with fuzzy logic, multipath load balancing clustering, hybridized bio-inspired routing, optimized Engroove-LEACH clustering GA + K-means routing, centralized clustering for mobility, and multi-criterion Binary Grey Wolf Optimizer (BGWO) clustering demonstrate varied yet complementary advancements. This paper consolidates theoretical frameworks, models, and the working principles of these algorithms, compares their performance trends, presents mathematical formulations, highlights limitations in computation, scalability, and parameter tuning, and outlines future research directions. The review also integrates multiple tables, equations, and conceptual analyses to provide a comprehensive understanding of modern energy-efficient routing in WSNs. This review adopts a PRISMA-based systematic methodology and proposes a unified taxonomy and comparative evaluation framework for energy-efficient routing protocols in Wireless Sensor Networks.