Energy Efficient and Secure Clustering and Routing Using Multi-Objective Trust Aware Adaptive Oscillation Weight Cheetah Optimization Algorithm

Wireless Sensor Nodes (WSN) consist of multiple distributed Sensor Nodes (SN) that gather and transmit data to Base Station (BS) for monitoring, processing, and analysis.The process of clustering organizes SN into groups to minimise energy consumption and enhance network stability, whereas routing facilitates data transmission to the BS.However, ensuring security and energy efficiency are challenging due to limited energy of non-rechargeable batteries in sensors.Therefore, secure cluster-based routing is crucial for energy efficiency in WSNs.This research proposes Multi-Objective Trust Aware Adaptive Oscillation Weight based Cheetah Optimization Algorithm (MO-TAAOWCOA) for energy-efficient and secure clustering and routing in WSNs.In traditional COA, AOW is incorporated by dynamically balancing local exploitation and global exploration capabilities, proving efficient for local optima issues with superior outcomes.The MO-TAAOWCOA is employed for the selection of optimal Cluster Heads (CHs) and route paths based on the distance among the neighbor nodes, distance between the CH and BS, residual energy, trust, node degree, and location factors through the model's multi-objective function.The proposed MO-TAAOWCOA consumes a lesser energy of 0.04J for scenario 4 in this research, thereby outperforming the existing Trust-aware Clustering and Routing Protocol (TCRP).