Real-Time Vehicle Tracking-Based Data Forwarding Using RLS in Vehicular Named Data Networking

Vehicular Named Data Networking (VNDN) is a new architectural paradigm that combines Named Data Networking (NDN) with Vehicular Ad Hoc Networks (VANETs). VNDN enables vehicles to request and receive named content in vehicle network environment. However, VNDN faces a major challenge of robust data forwarding due to the high mobility of vehicles, which causes frequent network disruptions and packet losses. To overcome this challenge, we propose a real-time vehicle tracking and mobility prediction scheme based on the Recursive Least Squares (RLS) estimation technique. The RLS model uses multiple parameters, such as received signal strength (RSS), GPS coordinates, and speed, to dynamically estimate the locations of vehicles in real-time. Roadside Units (RSU) use tracking information to predict the moving vehicle and forward data packets toward the target RSU based on its trajectories. Additionally, we employ a hop-by-hop feedback Congestion Control (CC) mechanism for VNDN environments to improve data forwarding efficiency in congested networks. We conduct extensive simulations to compare the performance of our proposed multi-parameter RLS-based scheme with existing schemes. The results show that our scheme improves the data delivery ratio by 20-35%, reduces the average content retrieval latency and control overhead by over 40%, and enhances the reliability of content delivery in highly dynamic VNDN environments. Our proposed scheme demonstrates the potential of RLS-based tracking for vehicular mobility prediction to enable robust data dissemination architectures and protocols for next generation vehicle networks.