MDDV

TLDR

Interest in leveraging information technology for surface transportation has grown, with a focus on on‑board sensing, computing, and vehicle‑to‑vehicle communications to enhance intelligent transportation systems. This paper proposes MDDV, a mobility‑centric data‑dissemination approach for vehicular networks that aims to operate efficiently and reliably amid high mobility and network partitioning. MDDV combines opportunistic, trajectory‑based, and geographic forwarding, using localized algorithms where vehicles act on local knowledge to achieve global dissemination, and its performance was evaluated through realistic Atlanta traffic simulations.

Abstract

There has been increasing interest in the exploitation of advances in information technology in surface transportation systems. One trend is to exploit on-board sensing, computing and communication capabilities in vehicles, e.g., to augment and enhance existing intelligent transportation systems. A natural approach is to use vehicle-to-vehicle communications to disseminate information. In this paper, we propose MDDV, a mobility-centric approach for data dissemination in vehicular networks designed to operate efficiently and reliably despite the highly mobile, partitioned nature of these networks. MDDV is designed to exploit vehicle mobility for data dissemination, and combines the idea of opportunistic forwarding, trajectory based forwarding and geographical forwarding. We develop a generic mobile computing approach for designing localized algorithms in vehicular networks. Vehicles perform local operations based on their own knowledge while they collectively achieve a global behavior. We evaluate the performance of the MDDV algorithm using realistic simulation of the vehicle traffic in Atlanta area.