A comparative performance analysis of vehicular routing protocols in intelligent transportation systems

Vehicular Ad hoc Networks (VANETs) are integral to the development of intelligent transportation systems, designed to enhance road safety, traffic management, and overall transportation efficiency. VANETs enable seamless communication between vehicles (Vehicle-to-Vehicle, V2V) and between vehicles and roadside units (Vehicle-to-Roadside, V2R), facilitating the exchange of critical data for real-time decision-making. Routing in VANETs has become a significant research focus due to its pivotal role in ensuring the reliable and timely dissemination of information, which directly impacts road safety. However, routing in VANETs faces numerous challenges, including high mobility, frequent network topology changes, scalability issues, complexity, and the need for robust security mechanisms to protect against malicious nodes. Furthermore, congestion and routing overheads in dense vehicular environments remain key obstacles. To address these challenges, advanced routing techniques have been developed, incorporating methods such as topology based routing, geo cast-based, geographic, cluster-based, broadcast, position - based, broadcast, and cross-layer routing, alongside optimization strategies using AI, game theory, and secure routing with cryptographic and blockchain-based solutions. Despite the proliferation of these techniques, there remains a lack of comprehensive and systematic reviews that analyze their performance in real-world scenarios. This assessment provides an in-depth evaluation of 118 state-of-the-art routing protocols specifically designed for VANETs, comparing them across various Quality of Service (QoS) criteria such as latency, reliability, scalability, and security. By highlighting the strengths, weaknesses, and key features of each protocol, this review offers valuable insights into the current state of vehicular routing. A coalition game-inspired clustering algorithm is designed to meet quality of service parameters. Additionally, it identifies critical research gaps and proposes future directions, focusing on the integration of emerging technologies such as edge computing, 5 G, and machine learning to further enhance the performance and security of smart vehicular systems.