ENERGY-EFFICIENT AND SAFE ROUTING WITH A COMBINATION OF PARTICLE SWARM OPTIMIZATION AND FUZZY SET

Wireless Sensor Networks (WSNs) are critical to modern IoT applications, yet their deployment is often constrained by limited energy, dynamic topologies, security vulnerabilities, and stringent Quality-of-Service (QoS) requirements. While existing approaches frequently address these challenges in isolation, this paper introduces a holistic routing framework that synergistically integrates an Improved Fuzzy Logic System (IFLS) with Particle Swarm Optimization (PSO) to balance multiple performance metrics in real time. Our hybrid model dynamically tunes routing parameters and fuzzy rules based on network state—including energy levels, congestion, node density, mobility, and security threats—thereby optimizing cluster-head selection, path stability, and trust-aware communication in UAV-assisted WSNs. Extensive simulations demonstrate that the proposed system achieves a 94.2% packet delivery ratio, reduces energy consumption by 48%, and extends network lifetime by 97% compared to contemporary fuzzy-based and trust-aware routing protocols. The work thus offers a scalable, adaptive, and energy-efficient routing solution suitable for large-scale, resource-constrained, and mobility-prone sensor networks. We also provide complete algorithmic specifications and reproducible simulation setups to facilitate validation and further research.