PDPD: Packet Delivery Prediction-based Data Forwarding to Moving Targets in Vehicular Networks

Abstract

Vehicular Ad hoc Network (VANET) is one of technologies to realize various ITS services that provide safe driving and efficient traffic condition. VANET consists of moving nodes, and hence its topology frequently changes. In VANETs, multi-hop data delivery is complicated by the fact that vehicular networks are highly mobile and frequently disconnected. In this thesis, we develop a novel forwarding scheme that accounts for the vehicle density, and delivers packets in a reliable and timely manner. We pay attention to the encounter event between two vehicles and the probability of successful transmission at the encounter place to guide forwarding decision. The proposed forwarding scheme uses traffic statistics to predict vehicle encounters, and optimize forwarding decision by taking into consideration the quality of wireless communications. We verify the results through simulations and show that our proposed scheme achieves reliable data transmission in VANET.