Localization Prediction in Vehicular Ad Hoc Networks

• Published in: IEEE Communications surveys and tutorials Vol. 20; no. 4; pp. 2784 - 2803
• Main Authors: Balico, Leandro N., Loureiro, Antonio A. F., Nakamura, Eduardo F., Barreto, Raimundo S., Pazzi, Richard W., Oliveira, Horacio A. B. F.
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2018
• Subjects: Data integration; Global Positioning System; Kalman filters; localization prediction; Target tracking; Time series analysis; time series prediction; Trajectory; Vehicular ad hoc networks
• ISSN: 2373-745X
• DOI: 10.1109/COMST.2018.2841901

Localization systems play a major role in many applications for vehicular ad hoc networks (VANETs). One of the most interesting problems to be solved in vehicular networks is how to provide anywhere and anytime highly accurate and reliable localization information. Unique characteristics of VANETs such as mobility constraints, driver's behavior, and the high-speed displacement nature of vehicles cause rapid and constant changes in network topology, leading to dissemination of outdated localization information. To circumvent this problem, 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 and used to take advantage of a future time-space window of a vectorial trajectory rather than a static localization point. In this paper, we discuss this subject by studying and analyzing the use of localization prediction as natural way to improve VANET applications. We survey proposed approaches for localization, target tracking, and time series prediction techniques that can be used to estimate the future position of a vehicle. We also highlight their advantages and disadvantages through an analytical discussion visualizing its potential application scenarios in VANETs. We present a set of experiments that show the results of such techniques when applied to a realistic VANET scenario.<xref ref-type="fn" rid="fn1"> 1 1 This work extends the original contribution proposed by Balico et al. <xref ref-type="bibr" rid="ref1">[1] . The current version includes several new contributions: a more complete description of the localization prediction methods and its application to VANETs; additional comparisons with other algorithms; and an analytical discussion of the localization prediction algorithms.