Optimizing Cluster Head Selection and Routing in 5G WSNs: A Reinforcement Learning and Deep Learning Approach

The Internet of Things (IoT) and 5G wireless sensor networks (WSNs) have transformed data transmission and inter-device communication; however, they face persistent routing challenges owing to energy constraints, latency, and packet loss. This study proposes an energy-efficient data transfer framework for IoT-based 5G WSNs by integrating a deep belief network (DBN) topology with a reinforcement learning (RL)-based clustering mechanism and Mantaray Foraging Optimization (MRFO) for multi-objective cluster head (CH) selection (energy, delay, traffic density, and distance). Unlike existing approaches, such as deep neural networks (DNNs) and time-temperature-dependent forwarding protocols (TTDFP), which focus narrowly on latency or energy efficiency, our hybrid DBN-RL-MRFO architecture jointly optimizes routing stability, scalability, and energy consumption. Simulations demonstrate that the proposed DBN-RL-MRFO framework reduces energy consumption by 5–10% compared to DNN-based methods and improves network lifetime (FND) by 5–15% over the TTDFP, while maintaining near-optimal throughput and latency. Although GEEC achieves lower energy use, our method balances energy efficiency with superior throughput (+3–8%) and reliability (PDR > 99.5 Statistical and complexity analyses further validate its robustness. This study advances reliable routing for IoT applications (smart cities, healthcare, and industrial automation) by balancing the trade-offs between critical WSN constraints.