AI-based Hybrid Clustering Method in Improving VANET Communication
Biju Bajracharya East Tennessee State University
Chandler Scott East Tennessee State University
Mohammad Khan East Tennessee State University
Abstract Intelligent Transportation Systems (ITSs) play a crucial role in enabling smart cities to operate efficiently, sustainably, and safely by facilitating communication of safety and non-safety messages. In ITSs, vehicles communicate with other vehicles (V2V) or infrastructure (V2I) to form Vehicular Ad Hoc Networks (VANETs). While Artificial Intelligence (AI) based clustering methods hold promise in enhancing VANETs, current implementations lack the flexibility to adapt to different environments. Specifically, these AI-based clustering approaches are centralized and unsuitable for real-time requirements for safety-critical message exchanges in VANETs. To overcome these limitations, vehicular network infrastructure must be adapted and tailored to anticipate traffic patterns and improve vehicular communications. A region-based hybrid network approach is promising for these cases. The hybrid approach enables vehicles to acquire region-specific knowledge and share it with the infrastructure, allowing the infrastructure to establish network policies for each region. By implementing this approach, we can improve VANET communications, overcome existing research limitations, and ensure the safety and efficiency of transportation systems in smart cities.