Previsão de Localização em Redes Ad Hoc Veiculares

Abstract

Localization systems play a major role in many applications for Vehicular Ad Hoc Networks (VANets). Although Data Fusion techniques can provide reliable localization information for most of the application requirements in VANets, enhancements on the localization systems are required and desirable. Unique characteristics of VANets such as mobility constraints, driver behavior, and high speed displacement nature of vehicles cause rapid and constant changes in network topology, leading to dissemination of outdated localization information. In this thesis, we identify that to circumvent the problem of dissemination of outdated localization information in VANets, an alternative is the use of predicted future locations of vehicles. The main idea of this approach is to use the localization prediction as an extension of a Data Fusion localization system. In such an approach, a future position of a vehicle is predicted for a given future time step and used in order to take advantage of a future time-space window of a vectorial trajectory rather than a static localization point. Thus, in this thesis we further discuss this subject by studying and analyzing the use of localization prediction as natural way to improve VANets applications. Using vehicles predicted locations as a metric for data communication in VANets, we propose a solution for the problem of dissemination of outdated localization informa-tion called LPRV (Localization Prediction-based Routing for VANets). In our proposed algorithm, packet forwarding is performed by nodes with predicted future localization closer to the delivery destination, without the need for exchanging additional control message. The proposed algorithm also explores the knowledge of a digital map to limit the scope of message exchanges in the shortest path for vehicles between source and destination.