Nadam-Swarm Based Adaptive Routing Protocol Using Graph Equivariant Network for Seamless Data Transmission in 5G-Connected Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have transformed data transmission methodologies by merging with 5G technology to provide ultra-reliable, low-latency, and energy-efficient data transfers. Nonetheless, owing to the intricacies involved in attaining dynamic network topologies, constrained resource management, and scalability, there is a want for improved routing methodologies to optimize 5G-enabled wireless sensor networks. This study introduces the “Nadam-Swarm based Adaptive Routing Protocol using Graph Equivariant Network for Seamless Data Transmission in 5G-Connected Wireless Sensor Networks” (NR-GE-BiSO) as a proficient solution for efficient data transmission. The protocol utilizes a multi-tiered approach: the Nadam-based Random Search Algorithm (NR-SA) dynamically allocates clustering head nodes to balance the load depending on the residual energy and traffic density of the nodes inside the network. Graph Equivariant Quantum Neural Networks (GE-QNN) provide a Wireless Sensor Network (WSN) structural graph to identify optimal routing pathways based on variations within the WSN, facilitating effective data delivery with minimal power consumption. The Bipolar Swarm Optimizer (BiSO) enhanced the routing process by determining the shortest, most energy-efficient routes with minimal latency and energy expenditure. Simulation results validate the efficacy of NR-GE-BiSO, achieving metrics: 99.92% throughput and a 99.88% packet delivery ratio with 99.01% reduction of routing overhead outperforming the existing methods. The findings indicated that the protocol facilitates energy-efficient, scalable, and reliable communication. By integrating 5G capabilities with advanced routing algorithms, NR-GE-BiSO achieves a heightened degree of wireless sensor network efficiency, enabling innovative applications in smart cities, industrial IoT, and environmental domains.