Trajectory Data Mining-Based Routing in DTN-Enabled Vehicular Ad Hoc Networks

被引：15
作者：
Qi, Weijing [1 ]
Song, Qingyang [1 ]
Wang, Xiaojie [2 ]
Guo, Lei [1 ]
机构：
[1] Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[2] Dalian Univ Technol, Sch Software, Dalian 116024, Peoples R China
来源：
IEEE ACCESS | 2017年 / 5卷
基金：
中国国家自然科学基金;
关键词：
Delay/disruption tolerant networks; time-space graph models; trajectory data mining; routing algorithm;
D O I：
10.1109/ACCESS.2017.2768485
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A delay/disruption tolerant network (DTN) architecture where a "store-carry-forward" strategy is adopted for data transmissions can be utilized in vehicular ad hoc networks (VANETs). The key point of routing in DTN-enabled VANETs is to choose the best node and determine the best time to forward messages. Time-space graph models provide an idea of converting the dynamic routing problems into static ones in deterministic DTNs. But it is a challenge to predict vehicles' future positions in order to obtain the time-space graph. In this paper, to achieve the cost-efficient and reliable routing in DTN-enabled VANETs, a novel timeliness-aware trajectory data mining algorithm is proposed to predict nodes' future positions. A sparse time-space graph is then obtained, based on which, two routing heuristics are proposed. Simulation results demonstrate that our proposed routing algorithms ensure low cost and high reliability over time.
引用
收藏
页码：24128 / 24138
页数：11
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