Enhancing wireless sensor network performance through self-tuned fuzzy logic, adaptive palm tree optimization, and Stackelberg Game-Theoretic load balancing: A comprehensive approach for energy efficiency, reliability, and security

A wireless sensor network (WSN) is a network of geographically scattered sensors that collects and transmits environmental data to a central processing station. Despite their extensive usage in environmental monitoring, military surveillance, and healthcare, WSNs present considerable challenges, including energy efficiency, network durability, and data transfer reliability. High energy consumption, irregular data throughput, packet loss, and lower network lifetime have detrimental effects on WSN performance, particularly in dynamic and large-scale configurations. This work introduces an innovative three-layer framework aimed at improving clustering, routing, and load balancing in Wireless Sensor Networks (WSNs). Initially, dynamic clustering is accomplished through a combination of Self-Tuned Fuzzy Logic and Adaptive Palm Tree Optimization (APTO), which considers energy, distance, throughput, and trust to effectively choose Cluster Heads (CHs). Next, the improved orbit optimization algorithm (IOOA) is utilized for selecting multi-hop routing paths and optimizing factors, such as energy usage, latency, and reliability. Finally, a Stackelberg Game-theoretic Approach (SGTA) is applied to ensure a fair load distribution among nodes, preventing overload, and boosting network stability. Together, these methods enhance the energy efficiency, reliability, and overall performance of the WSN. Simulation results demonstrate that the proposed approach, compared to existing algorithms such as the game-based dynamic clustering routing (GDCR) protocol, Game Theory-Based Fuzzy Routing Protocol (GTFR), Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT), game theory inter-cluster routing, improved ant colony optimization (GTIACO), enhancement game, and gray wolf algorithm (EG-GWA) protocol, decreases energy consumption, improves throughput, extends network lifespan, and provides stability and reliability. The proposed method achieves an energy consumption of 15 mJ, packet delivery ratio (PDR), 0.98 Mbps of throughput, and 0.32 ms of jitter.