SAGO-DLHS: Sleep-Awake based Grid Optimization with Dual-Level Head Selection in WSN

Wireless Sensor Networks (WSNs) primarily depend on the energy-efficient operations to ensure long-term deployment and effectiveness in real-world environments. Traditional cluster-based architectures often suffer from uneven energy consumption and high communication overhead. In this work, we propose a novel Sleep-Awake based Grid Optimization with Dual-Level Head Selection (SAGO-DLHS) algorithm designed specifically for grid-based WSN architectures. Unlike conventional clustering, our approach divides the network field into static grids and dynamically selects Grid Heads (GHs) and higher-level Zone Heads (ZHs) based on their residual energy and distance to the base station. A sleep-awake mechanism is additionally implemented within each grid to reduce the redundant energy consumption by keeping only few of the nodes active at a time. The selection of GHs and ZHs is driven by an optimization strategy that ensures balanced load distribution and efficient data transmission using hierarchical routing. Simulation results demonstrate significant improvements in network lifetime, residual energy distribution, and coverage ratio as compared to the traditional WSN approaches.