Energy Efficient Wireless Sensor Networks Through PUMA Based Clustering and Grid Routing

Energy efficiency and prolonged network lifetime remain key challenges in wireless sensor networks. Clustering, cluster head selection, and routing are central to addressing these issues since they directly affect energy consumption, data delivery, and overall network stability. In this work, we introduce a novel hybrid protocol named PUMA-GRID, which uniquely integrates the recent Puma Optimization Algorithm with a grid-based multi-hop routing framework. Unlike traditional schemes, PUMA-GRID adaptively balances exploration and exploitation during cluster head selection while learning optimal data forwarding paths through grid-based routing. This combination provides improved adaptability, scalability, and load balancing, key strengths that distinguish it from earlier AEO, LEACH, and static PUMA variants. The fitness function for cluster head election incorporates intra cluster distance, distance to the base station, and residual energy, with adjustable weights that allow flexible adaptation to deployment scenarios. Simulation experiments were performed under different base station placements and weight configurations to assess the influence of each factor. The results show that the effect of the weights depends strongly on base station location, and that careful tuning is required to balance efficiency and fairness. Across all scenarios, PUMA-GRID demonstrated superior performance compared to LEACH, AEO based schemes, and other PUMA variants Overall, PUMA-GRID demonstrates an effective and scalable solution for sustainable and energy-aware operation of wireless sensor networks.