Self Attention-based Sparse Graph Convolutional Neural Network for Instant Parameter Prediction to Optimize Wireless Sensor Network Performance in Electrical Systems

WSNs function as key components across environmental monitoring systems as well as healthcare facilities and industrial automation networks. WSN performance optimization faces ongoing challenges because the networks must deal with restricted energy supplies in addition to shifting operational environments and restricted data transfer capabilities. The current optimization techniques implement static hierarchical approaches combined with heuristic methods yet these prove unable to adjust in real-time thus leading to deteriorating network conditions and resource inefficiency. The research puts forward Self Attention-based Sparse Graph Convolutional Neural Network (SA-SGCN) as an instant parameter prediction framework to optimize WSN performance through automatic adjustments of critical metrics including energy usage and packet success ratios as well as latency and data transfer speed. Real-time prediction becomes possible through the self-attention mechanisms used in SA- SGCN models to detect distant dependencies in the sensor network in combination with sparse graph convolution that decreases computational requirements. The Crested Porcupine Optimizer (CPO) optimizes the hyperparameters of the SA- SGCN model for better efficiency and accuracy measurement. The experimental evidence shows that using the proposed method results in superior performance compared to current approaches because it reaches a prediction accuracy level of 99.9% for WSN configurations. The research presents a dependable energy-efficient adaptive framework for WSNs which enables high scalability and reliable operation within fast-changing environments.