Trust-Energy Path-Reinforcement Routing for Secure and Sustainable Wireless Sensor Networks under Dynamic Malicious-Node Conditions

Abstract

WSNs (wireless sensor networks) in hostile environments not only have limited energy resource but also have the existence of malicious node behaviour which significantly degrades both the routing reliability and network lifetime quality. To overcome this challenge, this study presents a trust-energy reinforced routing framework for secure and energy aware communication in WSNs with dynamic malicious-node conditions. The proposed method integrates residual energy, trust value as well as route-reinforcement memory into a single mechanism for next-hop selections, allowing routing decisions to adapt to both the earlier successful paths as well as the current network conditions. A MATLAB-based comparative simulation study through a network of 100 sensor nodes, malicious nodes being updated periodically, was conducted and the performance of the proposed simulated approach was evaluated against Grey Wolf Optimization (GWO)-, Harris Hawks Optimization (HHO)- and Whale Optimization Algorithm (WOA)-inspired routing approaches. In the experiments, the proposed method achieved best results in terms of final trust value, residual energy, alive-node fraction, and average routing cost. It finally produced a trust value of 0.8773, residual energy 0.242 Joule, alive-node fraction 0.94, and routing cost of 146.76. In addition, the first node death was delayed to round 28, while the half-nodes-dead and last-node-death conditions were not reached within the 50-round simulation horizon. These findings confirm that the proposed framework provides a more balanced and effective solution for secure, energy-efficient, and sustainable routing in adversarial WSN environments.