An efficient and robust clustered data gathering protocol (CDGP) for vehicular networks

Bouziane Brik, Nasreddine Lagraa, Mohamed Bachir Yagoubi, Abderrahmane Lakas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Data collection in Vehicular Ad-hoc Networks (VANets) is considered as an interesting application which aims at delivering road traffic, environmental information and other kinds of data to interested entities. The protocols developed for the wireless mobile networks and particularly for WSN cannot be applied directly to vehicular networks due to the high mobility and the frequent disconnection especially in the highway. Therefore, the design of an efficient protocol is depending firstly on the use of an efficient technique for medium access and secondly on a correct data delivery. In this paper, we propose a data collection protocol based on clustering and on he use of a dynamic medium access technique. This protocol is based on acknowledged delivery and allows retransmission of data in the case of loss or errors. We present simulation results which show the feasibility, the effectiveness and the robustness of our proposed protocol.

Original languageEnglish
Title of host publicationDIVANet'12 - Proceedings of the ACM Workshop on Design and Analysis of Intelligent Vehicular Networks and Applications
Pages69-74
Number of pages6
DOIs
Publication statusPublished - 2012
Event2nd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2012 - Paphos, Cyprus
Duration: Oct 21 2012Oct 22 2012

Publication series

NameDIVANet'12 - Proceedings of the ACM Workshop on Design and Analysis of Intelligent Vehicular Networks and Applications

Other

Other2nd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2012
Country/TerritoryCyprus
CityPaphos
Period10/21/1210/22/12

Keywords

  • CDGP
  • D-SDMA
  • Data collection
  • VANet

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Automotive Engineering

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