Offers a detailed analysis of optimization algorithms and routing protocols are created to overcome the issues on energy efficiency with the Internet of Things (IoT) Enabled Wireless Sensor Networks (WSNs). The study analyses nature-based metaheuristic methods such as the Genetic Algorithms, Particle Swarm Optimization, Firefly Optimization, Gray Wolf Optimization and Water-Cycle Algorithms, and specialised protocols of clustering, routing and data aggregation. Both approaches address such important issues as poor cluster head selection, energy disproportion, data duplication, network overloading, and early node failure that affect network lifespan and performance adversely. The research examines the energy optimization achieved by these algorithms in the form of intelligent cluster arrangements, traffic conscious routing, task scheduling processes and data aggregations. Particular attention is focused on the resource constrained contexts in which it is not viable to swap batteries, such as smart agriculture and smart cities. The discussion shows that hybrid metaheuristic solutions with improved optimization solutions can do better in terms of meeting several goals such as minimizing energy usage, improving throughput, increasing the ratio of packets delivered and Quality of Service demands. This survey can supply useful information about the development of energy-saving solutions and define new tendencies in the optimization of IoT networks.
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