Deep Learning Based Routing and Clustering Approaches for Energy Optimization in 5G Wireless Sensor Networks: A Review

Next-generation Wireless Sensor Networks (WSNs), including 5G and Beyond-5G networks, have made significant progress. However, their development requires re-evaluating intelligent communication approaches to meet the growing demands for higher data transmission rates, more efficient spectral utilization, and reduced energy consumption. Scalability issues related to energy efficiency remain a critical concern for WSNs, which remain integral to the digital revolution. This paper investigates the role of Deep Learning (DL) methods in improving energy efficiency for routing and clustering in 5G WSNs. It is pertinent to note that Deep Q-Networks (DQNs) and their variants have significantly enhanced Cluster Heads (CHs) selection schemes and routing techniques, thereby extending network lifetime by optimizing power consumption. Device-to-Device (D2D) communication is a key enabler of 5G WSNs, and recent studies have shown that combining DL with D2D can achieve significant performance gains. These approaches are critical to ensuring the implementation of intelligent, energy-efficient 5G WSNs. Future research should continue to support the development of hybrid models that integrate DL approaches and optimization techniques to adapt to dynamic network conditions in real-time communication systems.