Intelligent Energy‐Aware Routing for Next‐Generation Wireless Sensor Networks

With the evolution of 6G wireless networks, wireless sensor networks are facing new challenges, particularly when it comes to energy efficiency and reliable data transmission. This paper proposes an energy‐aware, intelligent routing framework that uses deep reinforcement learning to extend the lifetime of networks and increase data throughput in 6G networks. Through the implementation of a deep recurrent Q‐learning mechanism, the framework enables dynamic routing with residual energy and node proximity as criteria for selecting the next hop, either in single‐hop or multi‐hop scenarios. As demonstrated by experimental results, the proposed model delivers higher packets, consumes less energy, and has a lower latency while achieving greater throughput than conventional PSO or clustering‐based methods. WSNs of the future can take advantage of its robust routing capabilities.