Trust‐Aware and Optimization‐Driven Secure Routing in Heterogeneous Wireless Sensor Networks for Internet of Things Applications

ABSTRACT In the context of IoT applications, heterogeneous Wireless Sensor Networks (WSNs) face significant challenges related to energy efficiency and secure routing. Traditional secure routing protocols, although reliant on encryption and decryption mechanisms, often suffer from high key generation times, large encrypted data sizes, and slow decryption speeds, leading to performance degradation and energy inefficiency. This research proposes a novel trust‐aware and energy‐efficient routing protocol that dynamically optimizes node selection and routing paths in adversarial environments. The model incorporates a dynamic trust evaluation mechanism, likely point particle swarm optimization (LPPSO) for trustworthy cluster head (CH) selection, and streamlined genetic algorithm (SGA) for adaptive, energy‐aware routing. Experimental results implemented using MATLAB 2021a show the proposed approach maintains strong performance under attack conditions, with packet delivery ratio (PDR) decreasing from 98.5% to 93.1%, throughput reducing from 3.42 to 2.85 Mbps, end‐to‐end delay increasing from 0.3396 to 0.4821 ms, and network lifetime dropping from 1200 to 1080 rounds—wormhole attacks having the most significant impact. The study concludes that integrating trust‐based security with optimization techniques substantially enhances the robustness, lifetime, and efficiency of WSNs in IoT environments.