A K-Means Clustering and Artificial Bee Colony Algorithm-based Routing Framework for Dynamic Communication Networks

Abstract

: To guarantee dependable and timely data transfer, dynamic communication networks including Mobile Ad Hoc Networks (MANETs), Wireless Sensor Networks (WSNs), Internet of Things (IoT) system, and Vehicular Ad Hoc Networks (VANETs) need effective routing techniques. Because of node mobility, network congestion, energy limitations, and frequent topology changes, conventional routing protocols frequently suffer from performance degradation. This study suggests and optimized routing architecture for dynamic communication networks that combines the Artificial Bee Colony (ABC) optimization method with K-Means clustering in order to overcome these difficulties. Network nodes are arranged into effective clusters using the K-Means clustering technique. Which lowers routing overhead and increases network scalability. The Artificial Bee Colony method takes into account factors including residual energy, communication distance, link quality, and network congestion to determine the best cluster heads and find effective routing patterns. Enhancing route stability, minimizing packet loss, lowering end-to-end latency, and improving network performance are the goals of the hybrid framework. The suggested method enables effective resource use and allows adaptive decision-making in the face of shifting network conditions. Performance measures such Packet Delivery Ratio, throughput, Energy Consumption, Routing Overhead, and Network Lifetime will be used in simulation tests to assess the framework efficacy. The anticipated outcomes show that in dynamic communication contexts, the suggested hybrid routing model can greatly outperform optimization-based routing techniques and conventional routing protocols.