Intelligent unequal clustering in wireless sensor networks using a game theoretic and evolutionary strategy

Traditional clustering methods in Wireless Sensor Networks (WSNs) create energy distribution imbalances which shortens network operational time. The distribution of cluster heads becomes non-uniform because it does not respond to changes in node density or traffic patterns which leads to rapid energy exhaustion among specific nodes while other nodes remain idle. The unequal clustering technique offers adaptive node distribution and traffic load balance in response to this problem. The proposed method selects cluster heads through Coyote Optimization Algorithm (COA) optimization while fuzzy logic controls the adaptive cluster radii according to network conditions. Energy-efficient data routing is made possible through the implementation of a game-theoretic approach. Simulation analysis shows significant performance gains. The proposed method outperformed traditional EE-LEACH by extending network lifetime by 124.5% through its implementation. The network operational duration increases because the intelligent energy consumption management system distributes loads evenly across different network conditions. The proposed intelligent unequal clustering method demonstrates great potential to improve WSN performance. The combination of dynamic clustering with optimized routing systems leads to major improvements of both energy efficiency and network reliability as well as lifetime extension.