Exploiting Traveling Information for Data Forwarding in Community-Characterized Vehicular Networks

Zhong Li, Cheng Wang, Lu Shao, Chang Jun Jiang, Cheng-Xiang Wang

Research output: Contribution to journalArticle

Abstract

In intelligent vehicular communication networks, a hybrid communication architecture is used which combines both centralized and ad hoc transmission schemes. In order to maximize the end-to-end delivery ratio while reducing the network overhead, one important problem is to efficiently design the data forwarding algorithm to guarantee the quality of data transmission. In this paper, by considering the traveling information and vehicular space-crossing community structure, two metrics, 'space-time approachability' and 'social approachability,' are defined to provide the absolute and relative geographical information of the forthcoming contacts, respectively. Then, a novel data-forwarding algorithm, called approachability-based algorithm, is proposed, which utilizes two metrics together for better routing quality. We evaluate the proposed approachability-based algorithm utilizing San Francisco Cabspotting and Shanghai Taxi Movement datasets. Simulation results show that the approachability-based data forwarding algorithm can achieve better performance than the popular data forwarding algorithms ZOOM and BUBBLE RAP in all the interested scenarios.

Original languageEnglish
Pages (from-to)6324-6335
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume66
Issue number7
Early online date29 Nov 2016
DOIs
Publication statusPublished - Jul 2017

Keywords

  • Data forwarding
  • social community
  • traveling information
  • vehicular networks

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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