Metaheuristic Optimization for Energy Efficiency and Lifetime Extension in Wireless Sensor Networks: A Comprehensive Survey

Wireless Sensor Networks (WSNs) are essential to the IoT ecosystem but offer critical challenges or energy constraints impacting the viability of the network itself. The present paper provides a systematic review (2014-2024) metaheuristic optimization methods that have been developed to improve energy efficiency of WSNs. We will not only review various nature-inspired frameworks (e.g. PSO, ACO) but also the hybrid methods, dealing with NP-hard problems like cluster-head selection and energy aware routing. Our synthesis shows that metaheuristics outperform classical approaches by 25%- 40% on network lifetime via intelligent resource allocation. Our three main contributions consist of (1) a fresh new taxonomy of energy optimization problems as variants of WSN architecture, (2) an assessment of 32 metaheuristic solutions and their performance over various sizes of networks, and (3) attending to some trends; for example, machine learning-assisted optimization is an emerging trend, as is quantum-inspired algorithmic methodology.In spite of being effective to do so in controlled scenarios, serious shortcomings still exist: 85% of the methods reviewed assume static topologies, consequently ignoring that some settings involve changing (dynamic) topologies (i.e. real-world), and the associated computational costs (ex RDSAOA is almost 2-3×'s than the basic costs) are often too high for resource constrained nodes (wasted energy). Therefore, we advocate that every WSN application should seek to develop lightweight security augmented hybrids that are verified within dynamic environments (e.g. disaster zones,). We hope that this survey will provide researchers a favorable reference resource for building upon the next generation, energy efficient WSN protocols.