Adaptive Communication Mechanisms in VANETs: A Survey on Congestion Control, QoS Optimization, and Reliable Data Dissemination

Adaptive Communication Mechanisms in VANETs

Abstract

Vehicular Ad Hoc Networks (VANETs) constitute the wireless backbone of Intelligent Transportation Systems (ITS), supporting cooperative awareness, safety messaging, and dynamic traffic management. However, as vehicular density increases, limited radio spectrum and broadcast redundancy trigger severe channel congestion, degrading reliability and Quality of Service (QoS). This paper presents a comprehensive comparative study of adaptive communication mechanisms for congestion mitigation and network optimization. Classical, reactive, proactive, hybrid, and AI-driven methods are analyzed alongside emerging paradigms—Software-Defined Networking (SDN), Edge/Fog Computing, Federated and Reinforcement Learning, Blockchain-based trust, and 6G-enabled V2X. Mathematical models of adaptive data-rate and power control are discussed, and quantitative evidence from simulation studies is consolidated. Hybrid SDN-Edge architectures demonstrate up to 40 % throughput improvement and 35 % latency reduction under dense urban load. The paper concludes with a detailed research roadmap toward autonomous, trustworthy, and sustainable vehicular communication ecosystems.