Information theory based performance analysis and enhancement of Safety applications and cluster design in VANET

Authors

  • Zhongyi Shen Beijing University of Posts and Telecommunications image/svg+xml
  • Xin Zhang Beijing University of Posts and Telecommunications image/svg+xml
  • Meng Zhang Beijing University of Posts and Telecommunications image/svg+xml
  • Zhihao Chen Beijing University of Posts and Telecommunications image/svg+xml
  • Weijia Li Beijing University of Posts and Telecommunications image/svg+xml
  • Hongyu Sun Beijing University of Posts and Telecommunications image/svg+xml

DOI:

https://doi.org/10.4108/eai.1-1-2016.150808

Keywords:

Mobility, information theory, cluster, vehicular ad hoc network (VANET), Dedicated Short-Range Communication (DSRC)

Abstract

Safety applications in vehicular ad hoc network (VANET) are handled by broadcast to disseminate safety related messages, due to lack of stable topology. The mobility of vehicles leads to significant performance degradation, especially in dense and dynamic scenarios. This paper presents an information theory based mobility model to determine the theoretical amount of information for VANET safety applications. The new mobility model considers the safety distance and vehicle’s status. Analysis results are helpful in reducing redundant information and gaining more insight for system design. Based on the model, an adaptive algorithm to derive the optimal data rate is proposed. In addition, an adaptive control channel interval (CCI) algorithm is applied in cluster forming to improve stability of cluster topology. Numerical simulations based on NS-3 show that algorithms proposed can improve the performance dramatically, and the effectiveness of the safety requirements is guaranteed.

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Published

01-01-2016

How to Cite

Shen, Z. ., Zhang, X. ., Zhang, M. ., Chen, Z. ., Li, W. ., & Sun, H. . (2016). Information theory based performance analysis and enhancement of Safety applications and cluster design in VANET. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 3(6), e2. https://doi.org/10.4108/eai.1-1-2016.150808