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

Energy efficiency and prolonged network lifetime remain central challenges in wireless sensor networks (WSNs). Clustering, cluster-head (CH) selection, and routing are key to addressing these issues because they directly affect energy consumption, data delivery, and overall network stability. This paper introduces a novel hybrid protocol, PUMA-GRID, which integrates the recently proposed Puma Optimization Algorithm with a grid-based multi-hop routing framework. Unlike traditional schemes, PUMA-GRID adaptively balances exploration and exploitation during CH selection while learning energy-efficient data-forwarding paths through grid-based routing. This combination improves adaptability, scalability, and load balancing, which distinguishes PUMA-GRID from the primary metaheuristic competitor AEO-GRID, as well as earlier AEO, LEACH, and static PUMA variants. The fitness function for CH election incorporates intra-cluster distance, distance to the base station (BS), and residual energy, with adjustable weights that enable flexible adaptation to different deployment scenarios. Simulation experiments were performed under various BS placements and weight configurations to assess the influence of each factor. The results show that the impact of the weights depends strongly on BS location and that careful tuning is required to balance efficiency and fairness. Across all scenarios, PUMA-GRID demonstrates superior performance compared with LEACH, AEO-based schemes, and other PUMA variants. Overall, PUMA-GRID provides an effective and scalable solution for sustainable, energy-aware operation in WSNs.