A Novel Game Theory-Based Adaptive Resource Optimization Algorithm for Efficient Communication in WBSN

Abstract

Wireless Body Sensor Networks (WBSNs) have become an important part of putting continuous health monitors into operation and are affected by the shortage of energy, latency and dependable conveyance of information. This paper suggests a Game Theory-Based Adaptive Resource Optimization (GTARO) solution that aims to improve energy efficiency, radio communication stability and Quality of Service (QoS) within WBSNs. The approach combines node state sensing, utility-based coalitions and adaptive resource allocations to Nash Adaptive Equilibrium Routing (NAER) nodes next hop selection stability. A Dynamic Multi-Factor Optimization (DMFO) mechanism is applied by using firefly optimization and genetic operators to optimize end-to-end routing paths in terms of energy, reliability and latency compromises. The adaptive framework modifies the weights energy, load, priority and link quality. The results indicate that GTARO performs substantially better than the existing protocols. In particular, it generated the highest network lifespan, lower consumption of energy per round and it greatly minimized the latency. Further, GTARO performed best in packet delivery ratio and throughput in a dense WBSN, demonstrating its strength in such a scenario. The adaptive re-optimization method guaranteed stable performance in different traffic and topology process. This proves the effectiveness of GTARO in balancing between energy savings and QoS assurances, which makes it a viable tool in real-time healthcare monitoring routines.