Challenges and Solutions in VANET Routing: A Study of the Use of Adaptive Algorithms

Abstract

Vehicular Ad Hoc Networks (VANETs) are the backbone of the modern transportation system, which is used for intervehicle and infrastructure communication to provide safety, efficient traffic management, and enhanced safety. Routing in VANETs has several challenges: high mobility, dynamic topology, scalability, Quality of Service (QoS) constraints, security concerns, and resource limitations. Adaptive algorithms are showing promise to answer these issues because they use routing strategies that dynamically change according to time-varying network conditions. This paper reports on adaptive algorithm techniques, based on techniques using reinforcement learning, swarm intelligence, and machine learning in improving VANET routing. It further analyzes current routing algorithms, proposes a conceptual framework for adaptive routing, and provides further scope for future work on AI integration, 5G, and edge computing. The outcomes conclude that these algorithms could optimize VANET performance and result in efficient, scalable, and secure communication over the rapidly developing vehicular networks.