Evolutionary reinforcement learning framework for energy-efficient fault resilience and topological stability in WSNs

Wireless Sensor Networks (WSNs) form the technological foundation for several modern applications, including smart cities, environmental monitoring, healthcare surveillance, and industrial automation. However, the performance and long-term reliability of WSNs continue to be constrained by persistent challenges such as communication faults, accelerated energy depletion, reduced network lifetime, increased latency, and inconsistent throughput. These challenges are further intensified by the growing scale, density, and dynamic behavior of contemporary WSN deployments. Existing techniques frequently optimize one dimension-such as energy conservation or communication quality-while neglecting equally important aspects like fault recovery, adaptability, and real-time responsiveness. To address these limitations, this study introduces EvoGenRL, an Evolutionary Reinforcement Learning framework that integrates Reinforcement Learning (RL), Differential Evolution (DE), and Generative Adversarial Networks (GANs) for robust WSN management. In EvoGenRL, RL is utilized to learn adaptive routing and fault-tolerant decision policies; DE optimizes hyperparameters to improve convergence stability and policy efficiency; and GANs generate diverse, realistic fault scenarios to enrich the training environment and enhance model generalization. This combined strategy enables WSNs to operate efficiently under uncertain, heterogeneous, and failure-prone conditions. Experimental evaluations confirm that EvoGenRL delivers substantial improvements compared to conventional optimization and routing schemes. The proposed method successfully reduces energy consumption to 2.2 J, extends network lifetime to 1700 cycles, increases packet delivery ratio to 99.7%, lowers latency to 3.2 ms, and boosts throughput to 350 kbps. These advancements demonstrate the capability of EvoGenRL to simultaneously enhance energy efficiency, communication performance, and fault resilience. Overall, this research provides a comprehensive and scalable solution for next-generation WSN optimization. The EvoGenRL framework not only addresses current operational limitations but also establishes a foundation for future developments in intelligent, adaptive, and power-efficient sensor network control.