Smart Energy Management Leveraging Twin Adaptive Pulse Coupled Networks for Dynamic Energy Optimization in IoT-based Electrical WSN

Internet of Things (IoT)-driven Wireless Sensor Networks (WSNs) undergo fast growth hence requiring sophisticated energy optimization methods to keep the networks operational longer with reliable data handling. Traditional energy management practices lead to early node failure combined with inefficient network routes and non-even energy distribution which blocks network development and operational performance expansion. The three main factors that cause WSNs to be energy inefficient are improper node positioning along with excessive routing overhead and uneven distribution of power consumption across sensor nodes. The current network management approaches do not provide adequate dynamic energy distribution which results in premature network failure. This research establishes "Smart Energy Management leveraging Twin Adaptive Pulse Coupled Networks for Dynamic Energy Optimization in IoT-Based WSN (MG-TwinAPC-ReP)" to address these challenges. The proposed framework MG-TwinAPC-ReP features four layers which (1) strategic node deployment coverage, (2) Cluster-Based Routing Protocol Using Modified Greylag-Goose Optimization, and (3) Energy management through adaptive load balancing using Twin Adaptive Pulse Coupled Network's dual synchronization model and (4) uses Reformed Poplar Optimization to optimize networking parameters. Experimental results indicate remarkable performance capabilities which lead to a 99.82% increase in network lifetime and 99.74% energy conservation together with 99.91% reliable data delivery and 99.65% reduced latency compared to traditional IoT-WSN systems. The proposed scalable self-adapting energy-efficient WSN model provides an optimal solution for smart cities together with healthcare and agriculture and industrial IoT applications which drives sustainable IoT-driven WSN deployment into the future.