State Machine Based Clustering Algorithms in VANET

Recently, Vehicular Ad Hoc Network (VANET) is becoming an important topic of Intelligent Transportation Systems (ITS). However, in urban traffic, there are a large number of vehicles; this will result in heavy load of network, which may worsen the performance of VANET. This paper proposes clustered structure, aiming at reducing data redundant and improving network reliability. A centralized clustering algorithm based on Gur-Game is proposed, which is easy to perform while its stability is highly related to specific reward function. Then, an improved self-adaptive perception clustering method based on state machine is presented, which can work without roadside station distributed. The identity of vehicle is decided by neighboring cluster heads. This algorithm is more stable and can be suitable for various scenarios. Simulations on Paramics show the effectiveness of the above two algorithms.

• Record URL:
  • https://doi.org/10.1061/41177(415)115
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411773
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Wang, Qi
  • 0000-0002-9636-9659
  • Hu, Jianming
  • Wang, Yizhi
  • Zhang, Yi
• Conference:
  • First International Conference on Transportation Information and Safety (ICTIS)
  • Location: Wuhan , China
  • Date: 2011-6-30 to 2011-7-2
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 913-919
• Monograph Title: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation

Subject/Index Terms

• TRT Terms: Algorithms; Cluster analysis; Intelligent transportation systems; Simulation; Traffic congestion; Urban development; Vehicle to infrastructure communications; Vehicle to vehicle communications
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I70: Traffic and Transport;

Filing Info

• Accession Number: 01457502
• Record Type: Publication
• ISBN: 9780784411773
• Files: TLIB, TRIS, ASCE
• Created Date: Dec 19 2012 8:59AM