A Metaheuristic with Graph Sample and Aggregate Attention Network for Reliable Data Transmission and Fault Diagnosis in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used to monitor and collect data in environments with limited human access. Despite their potential, WSNs face persistent issues such as limited battery power, inefficient routing, and unreliable data caused by sensors or transmission faults. These challenges reduce the network lifetime and degrade the quality of the collected information. To overcome these limitations, this work introduces an integrated optimization framework that improves clustering, routing, and fault detection. The proposed approach adopts a density-driven clustering mechanism to form energy-balanced clusters and employs a Shrike Optimization Algorithm (SHOA) to identify suitable cluster heads by considering residual energy, node distribution, and trust levels. Routing paths are further refined using a Catch Fish Optimization Algorithm (CFOA), which dynamically selects energy-efficient and stable multihop paths. To ensure data reliability, a graph sample and aggregate attention network (GSAAN) was implemented to detect and isolate faulty data in real time. Simulation results demonstrated that the proposed framework consistently reduced energy consumption, extended network lifetime, and enhanced reliability compared with benchmark algorithms such as Mod PSO, CHBCO, IABC-C, HPO-WPBFT, and ASSO-SSO. Notably, it achieves a packet delivery ratio of 99%, fault detection accuracy of 98.7%, average delay of 155 ms, and network lifetime of up to 3400 rounds, demonstrating its effectiveness in critical WSN applications, including environmental monitoring and smart city deployments.