Optimization based energy aware scheduling in wireless sensor networks

In wireless sensor networks (WSNs), energy efficiency is a critical factor in extending network lifetime, particularly in applications involving multiple target tracking. This paper proposes a novel approach for sleep scheduling in WSNs using ant colony optimization (ACO) to achieve energy-aware scheduling while maintaining high tracking accuracy. The proposed method models the scheduling problem as an optimization task, where ACO is employed to dynamically adjust the sleep and active states of sensor nodes based on their energy levels and target detection requirements. By optimizing node activity, the algorithm minimizes energy consumption while ensuring continuous and reliable tracking of multiple targets. Experimental results demonstrate that the ACO-based scheduling approach significantly enhances network longevity and reduces energy depletion compared to traditional scheduling techniques without compromising tracking performance. This energy-aware solution is well-suited for real-time tracking applications in resource-constrained WSN environments, providing a balance between energy conservation and tracking precision.