An innovative dual-phased synergistic energy management approach for WSNs using enhanced sleep/awake scheduling and adaptive routing process

Wireless Sensor Networks (WSNs) have established themselves as one of the essential technologies in various applications, yet they face significant challenges due to their limited energy resources. To overcome this shortcoming, this work introduces an innovative dual-phased synergistic energy management approach that integrates enhanced sleep/awake scheduling based on Multi-Objective Particle Swarm Optimization with Crowding Distance (MOPSOCD) and Reservoir Computing (RC) based adaptive routing for optimizing energy consumption using dynamic real time-based node state adjustment mechanism. Experimental outcomes obtained through comprehensive simulations indicate that our proposed methodology achieves up to 32 % reduction in energy consumption per node, a 50 % improvement in extending network lifetime, and a 11 % enhancement in Packet Delivery Ratio (PDR) compared to state-of-the art algorithms. Additionally, the proposed method minimizes the computational overhead by 40 % which ensures reliability in dynamic environmental conditions. This outstanding performance is attributed to the intelligent integration of RC-driven energy predictions with adaptive routing and optimized clustering, which offers significant advancement in energy management strategies for WSNs, paving the path for sustainable and reliable network deployment.