Energy-efficient location-based routing protocol for wireless sensor networks using teaching-learning soccer league optimization (TLSLO)

Energy efficiency in wireless sensor networks (WSNs) is a crucial and fundamental design consideration. These networks typically consist of numerous small, resource-constrained sensor nodes, frequently placed in isolated or difficult-to-reach areas. This research presents a comprehensive methodology for improving the performance and energy efficiency of WSNs deployed in a designated target area. The research begins with the deployment of sensor nodes equipped with location information and the initialization of critical network parameters. Novel techniques are introduced for efficient node clustering using a Haversine-based K-means Clustering algorithm (HKMC) and an advanced hybrid optimization model, teaching-learning soccer league optimization (TLSLO), for optimal cluster head selection within clusters. Data aggregation at cluster heads is crucial for conserving energy, and data compression techniques, including the novel weighted discrete wavelet transform (WDWT)), are employed to reduce data transmission size. Furthermore, deep learning models in the form of recurrent artificial neural networks (RANN) predict energy consumption patterns, enabling the optimization of node sleep-wake schedules for a prolonged network lifetime. Simulated using Python, the proposed protocol’s performance is evaluated, demonstrating its superiority in terms of energy efficiency, latency, network lifetime, and data delivery ratio compared to existing routing protocols. This research offers a holistic approach to improving WSNs enhancing their efficiency and sustainability in resource-constrained environments.