Design Methods for the Cluster Head Selection in WSNs based on Node Residual Status to Enhance the Lifetime and Performance of the Network

Abstract

This study introduces a novel approach to enhance Wireless Sensor Networks (WSNs) by proposing an energy-balancing algorithm and a unique Cluster Head (CH) selection strategy based on nodes' residual energy states. Unlike conventional methods, our algorithm dynamically distributes energy across nodes, mitigating localized energy depletion and extending the overall network lifetime. The Knapsack method optimizes resource allocation considering energy constraints. Performance evaluations, conducted using NS2.34/2.35, compare our approach with a widely used energy-balancing technique in WSNs. Our results showcase significant improvements. Notably, our algorithm achieves a remarkable 20% increase in network lifetime and a 25% enhancement in data throughput, demonstrating its effectiveness in optimizing resource usage. Furthermore, a 30% reduction in latency ensures faster and more responsive data transmission. The CH selection method positively impacts network coverage, resulting in a substantial 20% expansion of monitored areas. Compared to the existing technique, our proposed strategy consistently outperforms in key metrics, indicating its superior efficacy. This research contributes to advancing WSN sustainability and efficiency, particularly in scenarios prioritizing energy efficiency. The proposed algorithm emerges as a promising solution, demonstrating its potential to optimize WSN performance and longevity. Furthermore, the network simulator (NS) is used to examine the performance of the network with the NS2.34/2.35 version. The results exhibit the dominance of the projected method compared to existing techniques. Additionally, the method shows adaptability to dynamic network conditions, ensuring effective CH reselection in the presence of node failures or energy fluctuations.