Energy-Aware Congestion Control Technique Based on Hybrid Weight-Based Optimization Algorithm in Wireless Mobile Communication Networks

Energy-aware congestion-free data routing is one of the critical challenges in successful data transmission for mobile sensor communication environments. Conventional techniques suffer from limitations such as high packet drop, ineffective Cluster Head (CH) selection, and unnecessary data retransmission, leading to high energy usage in mobile networks. To overcome these challenges, we propose Weight-based Prominent Wolf Preference Grey wolf optimization (2WP-GWO) with congestion control route selection through Separation Enhanced Elephant Herd Optimization algorithm (SEEHO). In this model, we enhance the efficiency of the standard GWO algorithm by prioritizing the location of the fittest wolves at each generation. Furthermore, we fix the priority level based on adopting weight measures to highly rank the fittest wolf (efficient CH), thereby lowering the influence of other mobile nodes over specific time intervals (generations). Additionally, congestion control is integrated into the SEEHO algorithm to find congestion-free paths for data transmission. This approach significantly improves network lifetime, reduces data traffic, and optimizes congestion-free optimal path selection. The proposed method is validated with other techniques using different performance measures, indicating promising results compared to other state-of-the-art approaches.