Hybrid Optimization and LearningBased Secure Routing for Sustainable Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely deployed in monitoring and control applications where reliable data delivery and prolonged network lifetime are critical. However, the limited energy capacity of sensor nodes and the presence of insecure or unreliable routing paths significantly degrade network performance. Existing solutions often address energy efficiency and security independently, resulting in suboptimal operation under dynamic network conditions. In this paper, a trust-aware and energy-efficient data transmission framework is proposed for WSNs by integrating hybrid metaheuristic optimization with a lightweight learning-based trust evaluation mechanism. The proposed approach jointly optimizes cluster head selection and routing path formation by considering residual energy, node trustworthiness, inter-node distance, and link quality. A multi-objective fitness function guides the optimisation process, balancing energy consumption and ensuring secure communication. Experimental evaluation using a benchmark WSN dataset demonstrates that the proposed framework significantly reduces energy consumption, extends network lifetime, and improves data confidentiality and data integrity compared with state-of-the-art routing techniques. The results confirm the suitability of the proposed method for secure and sustainable WSN deployments.