Trust-Based Multi-Objective Cluster Head Selection for Optimal and Secure Routing in Wireless Networks

Abstract

Wireless sensor networks, integral to applications such as healthcare, environmental monitoring, and defense, operate under constraints of energy, security, and data reliability, making optimization essential. These networks face challenges such as limited battery life, susceptibility to attacks, and maintaining efficient data transmission in dynamic environments. This research introduces a comprehensive trust-based multi-objective optimization framework for cluster head selection and secure routing, addressing these critical issues. The framework leverages the trusted weight-based hyperbolic sine bobcat optimizer (TWHSBO) for optimal cluster head selection, ensuring energy efficiency, and trust in the network. For secure and efficient data transmission, the Trusted Deep Reinforced-Q Zonal Network (TDRQZN) is employed, combined with the bluefin trevally optimizer (BTO) for optimal path selection. The proposed hybrid framework demonstrates substantial improvements in key performance metrics compared to traditional approaches. Performance metrics such as network lifetime increases by 40%, throughput improves by 30%, and packet loss reduces by 25%, ensuring reliable and secure data transmission. Energy efficiency is achieved through optimized resource allocation, extending the operational lifespan of sensor nodes. The integration of trust mechanisms in the proposed framework mitigates security risks, enhancing data integrity and privacy in sensitive environments.