Mobility Trace Analysis for Intelligent Vehicular Networks: Methods, Models, and Applications

被引:13
|
作者
Celes, Clayson [1 ,2 ]
Boukerche, Azzedine [1 ]
Loureiro, Antonio A. F. [2 ]
机构
[1] Univ Ottawa, Dept EECS, 800 King Edward Ave, Ottawa, ON K1N 6N5, Canada
[2] Univ Fed Minas Gerais, 6627 Pres Antonio Carlos Ave, BR-31270901 Belo Horizonte, MG, Brazil
基金
加拿大自然科学与工程研究理事会; 巴西圣保罗研究基金会;
关键词
Vehicular networks; vanet; data mining; data analysis; survey; routing; topology; mobility; IMPROVES DATA DELIVERY; TRAJECTORY SIMPLIFICATION; COMMUNITY STRUCTURE; WIRELESS NETWORKS; ROUTING BACKBONE; COMPLEX NETWORKS; SOCIAL NETWORKS; SMART CITY; BUS SYSTEM; INFORMATION;
D O I
10.1145/3446679
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.
引用
收藏
页数:38
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