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Wireless Networks

, Volume 20, Issue 5, pp 1121–1132 | Cite as

Joint delay and energy model for IEEE 802.11 networks

  • Stepan IvanovEmail author
  • Dmitri Botvich
  • Sasitharan Balasubramaniam
Article

Abstract

Popularity of IEEE 802.11 networks has increased dramatically over the past number of years. Nowadays audio/video conferencing, gaming and other quality of service (QoS) sensitive services are being delivered to the end users over wireless. Commonly probability for a packet to overstay a specific timeout serves as a QoS metric, and obtaining media access control layer packet delay distribution is highly important for this QoS prediction. Usually wireless devices are equipped with energy supplies of limited capacity, and accurate estimation of their energy expenditure is essential from the network design point of view. Meanwhile, as packets of longer delay normally have higher energy transmission cost, there is a certain dependency between the two metrics. This paper considers internal structure of the metrics and proposes a mathematical model that allows obtaining their individual distributions together with the joint distribution. The model presents a random sum, where the summand formation is determined by a Terminating Markov Process. The model has been validated through comparison with results of NS3 simulation.

Keywords

IEEE 802.11 Packet delay Energy consumption Modeling Joint distribution 

References

  1. 1.
    IEEE 802.11 Working Group. (2007). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. USA: The Institute of Electrical and Electronics Engineers, Inc.Google Scholar
  2. 2.
    Chen, Y., Zhang, S., Xu, S., & Li, G. (2011). Fundamental trade-offs on green wireless networks. IEEE Communications Magazine, 49(3), 30–37.CrossRefGoogle Scholar
  3. 3.
    Bianchi, G. (2000). Performance analysis of IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535–547.CrossRefGoogle Scholar
  4. 4.
    Ergen, M., & Varaiya, P. (2008). Throughput analysis and admission control in IEEE 802.11a. IEEE Transactions on Vehicular Technology, 57(1), 436–447.CrossRefGoogle Scholar
  5. 5.
    Zheng, Y., Lu, K., Wu, D., & Fang, Y. (2006). Performance analysis of IEEE 802.11 DCF in imperfect channels. IEEE Transactions on Vehicular Technology, 55(5), 1648–1656.CrossRefGoogle Scholar
  6. 6.
    Lu, K., Wang, J., Wu, D., & Fang, Y. (2009). Performance of a burst-frame-based CSMA/CA protocol: Analysis and enhancement. Wireless Networks, 15(1), 87–98, ISSN: 1022-0038.Google Scholar
  7. 7.
    Tainaka, T., Masuyama, H., Kasahara, S., & Takahashi, Y. (2009). A Markovian approach to per-flow throughput unfairness in IEEE 802.11 multihop wireless networks. Journal of Industrial Management Optimization, 5(3), 493–510.CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Hui, J., & Devtsikiotis, M. (2005). A unified model for the performance analysis of IEEE 802.11e EDCA. IEEE Transactions on Communications, 53(9), 1498–1510.CrossRefGoogle Scholar
  9. 9.
    Taher, N., Doudane, Y., & El Hassan B. (2009). A complete and accurate analytical model for 802.11e EDCA under saturation conditions. In Proceedings of the IEEE/ACS international conference on computer systems and applications.Google Scholar
  10. 10.
    Tinerello, I., & Bianchi, G. (2010). Rethinking the IEEE 802.11e EDCA performance modeling methodology. IEEE/ACM Transactions on Networking, 18(2), 540–553.CrossRefGoogle Scholar
  11. 11.
    Tainaka, T., Masuyama, H., Kasahara, S., & Takahashi, Y. (2011). Performance analysis of burst transmission mechanism for IEEE 802.11-based multi-hop wireless LANs. IEEE Transactions on Wireless Communications, 10(9), 2908–2917.CrossRefGoogle Scholar
  12. 12.
    Qi, H., Malone, D., & Botvich, D. (2009). 802.11 wireless LAN multi player game capacity and optimization. In Proceedings of the the 8th annual workshop on network and systems support for games.Google Scholar
  13. 13.
    Huang, K., & Duffy, K. (2009). On a buffering hypothesis in 802.11 analytic models. IEEE Communications Letters, 13(5), 312–314.CrossRefGoogle Scholar
  14. 14.
    Liu, R.P., Sutton, G., & Collings, I. (2010). A New queuing model for QoS analysis of IEEE 802.11 DCF with finite buffer and load. IEEE Transactions on Wireless Communications, 9(8), 2664–2675.CrossRefGoogle Scholar
  15. 15.
    Zhao, H., Wang, S., Xi, Y., & Wei, J. (2010). Modeling intra-flow contention problem in IEEE 802.11 wireless multi-hop networks. IEEE Communications Letters, 14(1), 18–20.CrossRefGoogle Scholar
  16. 16.
    Zhao, H., Garcia-Palacios, E., Wang, S., Wei, J., & Ma, D. (2013). Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks. Ad Hoc Networks, 11(1), 54–69.CrossRefGoogle Scholar
  17. 17.
    Tickoo, O., & Sikdar, B. (2008). Modeling queuing and channel access delay in unsaturated IEEE 802.11 random access MAC based wireless networks. IEEE/ACM Transactions on Networking, 16(4), 878–891.CrossRefGoogle Scholar
  18. 18.
    Xie, L., Wang, H., Wei, G., Xie Z. (2009). Performance analysis of IEEE 802.11 DCF in multi-hop ad hoc networks. In Proceedings of the international conference on networks security, wireless communications and trusted computing, pp. 227–230.Google Scholar
  19. 19.
    Kafetzakis, E., Kontovasilis, K., & Stavrakakis, I. (2011). Effective capacity-based stochastic delay guarantees for systems with time-varying servers, with an application to IEEE 802.11 WLANs. Performance Evaluation, 68, 614–628.CrossRefGoogle Scholar
  20. 20.
    Kafetzakis, E., Kontovasilis, K., & Stavrakakis, I. (2012). A novel effective capacity-based framework for providing statistical QoS guarantees in IEEE 802.11 WLANs. Computer Communications, 35, 249–262.CrossRefGoogle Scholar
  21. 21.
    Brandwajn, A., & Begin, T. (2009). A note on the effects of service time distribution in the M/G/1 queue. In SPEC benchmark workshop, pp. 138–144.Google Scholar
  22. 22.
    Ivanov, S., Botvich, D., & Balasubramaniam, S. (2010). Joint throughput and packet loss probability analysis of IEEE 802.11 networks. In Proceedings of the IEEE symposium on computers and communications.Google Scholar
  23. 23.
    Ivanov, S., Botvich, D., & Balasubramaniam, S. (2011). On delay distribution in IEEE 802.11 wireless networks. In Proceedings of the IEEE symposium on computers and communications.Google Scholar
  24. 24.
    Hida, T., & Hitsuda, M. (1993). Volume 120 of translations of mathematical monographs: Gaussian processes. USA: American Mathematical Society.Google Scholar
  25. 25.
    NS-3.13. The NS-3 discrete-event network simulator. http://www.nsnam.org/. Accessed December 2011.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Stepan Ivanov
    • 1
    Email author
  • Dmitri Botvich
    • 1
  • Sasitharan Balasubramaniam
    • 2
  1. 1.Telecommunications Software and Systems GroupWaterford Institute of TechnologyWaterfordIreland
  2. 2.Department of Electronics and Communications EngineeringTampere University of TechnologyTampereFinland

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