Improved Sensor Network Lifetime Using Fuzzy Scheduling and Hybrid Goshawk Hunger Games Optimization

In recent times, wireless sensor networks are constrained by limited node, dynamic topology, and uneven cluster formation, which affect energy-efficient routing, balanced load distribution, and reliable data transmission. To address these challenges, this research introduces a Fuzzy Logic-based Multipath Hybrid Goshawk Game Search Optimization framework for energy-efficient clustering and routing. The Hybrid Goshawk Game Search Optimization algorithm is employed to select optimal cluster heads by integrating the global exploration capability of Northern Goshawk Optimization with the adaptive local exploitation efficiency of Hunger Games Search algorithm, where the hybrid interaction is governed by a fitness-driven adaptive mechanism that balances exploration and exploitation, thereby improving convergence stability. The Fuzzy Logic System is designed for dynamic sleep scheduling using residual energy, node degree, and distance to the cluster head, which reduces redundant energy consumption and enhances load balancing. Moreover, the Hybrid Goshawk Game Search Optimization-based multipath routing mechanism is designed to establish a reliable and energy-aware communication path by considering hop count, signal strength, buffer rate, and residual energy. Simulation results demonstrate that the proposed model achieves superior performance with a higher packet delivery ratio of 98.89%, throughput of 350 Mbps, and reduced energy consumption of 36J compared to existing methods. These results emphasize that the proposed model provides a robust and scalable solution for efficient data transmission in a large-scale and dynamic wireless sensor network environment.