An Intelligent Framework for Stable Clustering in VANET Using Kohonen Network, Reinforcement Learning, and Multi-Criteria Optimization

Rapid vehicle movement and changes in overall vehicle density and distribution can make routing inefficient, cause delays, and create cluster instability while using VANETs (Vehicular Ad Hoc Networks). This paper intelligent frameworks focus on enhancing cluster stability and divides it into two stages: (1) the sophisticated weighted K-Medoid algorithm for signal strength and relative speed, node density, and movement direction-based initial clustering (2) dynamic weight adjustment self-organizing map (SOM) cluster head selection reinforcement learning (RL) optimization. NS-3 simulations (node density 50–150, speed 10–30 m/s) demonstrate the frameworks Adaptive RL border patrol outperformed SDPC, RL-neighbor selection, K-means, fuzzy logic with 9% SDPC, 15% K-means more PDR, improved end-to-end delay, cluster linger time, control traffic and greater RC. Adding RL improved adaptability and stabilization in high mobility fluctuating mover scenarios.