Vehicle-Assisted Data Delivery Based on Trajectory Prediction

Abstract

This work proposes a novel vehicle-assisted data delivery algorithm called VDDTP. VDDTP creates an extended trajectory model and uses predicted road-network constrained trajectories to calculate packet delivery probabilities. Next, it applies the predicted trajectories and some proposed heuristics in a data forwarding strategy to improve the vehicular network's global metrics (i.e., delivery ratio, communication overhead, and delivery delay). We perform extensive experiments using a real-world and large-scale trajectory dataset for evaluating vehicular network applications. The results demonstrate the algorithm's ability to improve the global metrics compared to related work.