Investigating QoS in Mobile Ad Hoc Networks through Scikit-Learn K-Means Clustering: A Performance-Oriented Approach
Minal Patil, Manish Devendra Chawhan, Abhishek Madankar, Ashish Bhagat, Bhumika Neole · International Journal of Electrical and Electronics Engineering · 2025
Mobile Ad-Hoc Networks are incapable of energizing themselves due to their limited energy. The effort is to develop an energy-adequate power management plan for the MANET. Cluster heads may malfunction or operate incorrectly as a result of power problems while based on different cluster routing methods. Consequently, during information collecting and interaction, the cluster heads encounter instability. Finding the unstable cluster heads and swapping out for another node to use the re configurable clustering technique is the primary goal of this study. In order to correctly define the cluster heads, the proposed a Scikit-Learn's K-Means clustering method. The anomalous or superfluous modifications in cluster heads and the shift in the cluster nodes in the count are detected by the suggested in Scikit-Learn's K-Means technique. The proposed work represents Scikit learning k-means clustering in MANET and calculates QoS parameters such as Energy, Throughput, Delay and Packet delivery ratio. By leveraging Scikit-Learn's K-Means Clustering (SLKMC), a novel approach in MANET can achieve an optimized trade-off between energy, delay, PDR, and Throughput, making it a practical and efficient choice for QoS enhancement as Energy efficiency is expected to be up to 10-30% energy savings by reducing redundant communication. The delay is reduced with anticipation of a 15-25% decrease in average delay with efficient cluster-based routing. Packet Delivery Ration might improve by 5-20%, ensuring more reliable data delivery. Throughput is improved coordination and reduced collisions can enhance Throughput by 10-25%. Thus, the expected benefits quantify the impact of these enhancements in terms of QoS metrics and improve Network Performance.