Propagation and delay-optimal safety messaging in vehicular networks

Vehicles that talk to each other are expected to unleash a broad spectrum of applications that will make road travel safer, faster, efficient and more entertaining. We study safety messaging, which enables applications that provide different levels of driver assistance to improve on-road safety, for different vehicular network scenarios. Initial deployments of safety applications in vehicles are expected to be sparse with support for event-driven messaging over a few wireless hops, for example, a car broadcasting messages when in distress. We propose GeoMAC, a protocol that exploits spatial diversity of forwarder nodes, and a geo-backoff mechanism to resolve contention between them, and achieves message delivery with smaller latency and jitter, and greater reliability. Eventually, all on-road vehicles will broadcast their state information, such as location and velocity, many times a second. Each vehicle’s state must be received in a timely manner and be refreshed periodically at all other vehicles of interest. The network objective of minimizing the system age, which we define, is then explored for single and multi-hop networks. For single-hop networks we assume a carrier-sense-multiple-access (CSMA) based sharing of the wireless medium. We show that the minimum system age cannot be achieved in 802.11 networks through pure MAC techniques. We propose, and evaluate on ORBIT, an application broadcast rate adaptation algorithm that allows nodes to locally adapt their messaging rate to keep the system age to a minimum. Next, we explore the benefits of a multi-hop wireless connectivity for a given physical network of on-road vehicles, when nodes can piggyback other nodes’ states. The system age optimization is formulated for arbitrary network graphs and round robin schedules. We show that, under certain conditions, significant improvements in system age may be obtained. For tree topologies, an algorithm that gives schedules that minimize system age is proposed. We end our study on messaging with an empirical evaluation of how enabling location prediction can help reduce rate of messaging and hence channel congestion. Finally, we measure and model the effect of a car’s own geometry, antenna placement, and of other cars in vicinity, on the vehicle-to-vehicle link and the network.Ph. D.Includes bibliographical referencesby Sanjit Krishnan Kau