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Wireless Personal Communications

, Volume 72, Issue 2, pp 987–1004 | Cite as

Discrete-time Markov Model for Wireless Link Burstiness Simulations

  • Yantao Li
  • Daniel Graham
  • Gang Zhou
  • Xin Qi
  • Shaojiang Deng
  • Di Xiao
Article

Abstract

Link burstiness can negatively affect the performance of wireless networking protocols, by causing an extra of 15 % transmission cost. It describes the underlying behavior of packet delivery and provides insights into tuning protocols to improve performance. In this paper, we propose a discrete-time Markov model to simulate the burstiness behavior of wireless links, which provides a novel approach for link burstiness studies. More specifically, we first present a discrete-time Markov model with the input of \(\beta \) value and the output of a sequence trace of burstiness traffic. Then we design an algorithm to simulate the Markov model, where the state transition represents the packet receptions or losses. Finally, we evaluate the proposed model in terms of distribution of link burstiness, accuracy and cost, and the results demonstrate that our model is able to accurately simulate the burstiness behavior.

Keywords

Simulation Low power wireless networks Discrete-time Markov model Link burstiness 

Notes

Acknowledgments

This work was supported in part by US National Science Foundation (Grant nos. ECCS-0901437, CNS-0916994), the National Natural Science Foundation of China (Grant nos. 61173178, 61070246, 61003247), the Natural Science Foundation Project of CQ CSTC (Grant No. 2011jjjq40001), the Program for New Century Excellent Talents in University of China (Grant nos. NCET-09-0838, NCET-08-0603), the Fundamental Research Funds for the Central Universities (Grant nos. CDJZR10180002, CDJZR10180003, CDJXS12180004), and the Natural Science Foundation Project of CQ CSTC (Grant nos. 2010BB2047, 2010BB2210, 2009BB2211).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yantao Li
    • 2
    • 1
  • Daniel Graham
    • 2
  • Gang Zhou
    • 2
  • Xin Qi
    • 2
  • Shaojiang Deng
    • 3
  • Di Xiao
    • 3
  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.Department of Computer ScienceThe College of William and MaryWilliamsburgUSA
  3. 3.College of Computer ScienceChongqing UniversityChongqingChina

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