Advertisement

A practical sleep coordination and management scheme with duty cycle control for energy sustainable IEEE 802.11s wireless mesh networks

  • Hadi Barghi
  • Seyed Vahid Azhari
Article

Abstract

We consider the energy sustainable operation of solar powered IEEE 802.11s wireless mesh networks. Our main contribution is the development of a simple and novel sleep scheduling scheme that is local and distributed and provides contiguous sleep intervals that can be used for putting both radio interface cards and the main-board into deep sleep mode. We show this provides substantial energy savings as main-board power consumption comprises a significant portion of total node power. Unlike many sleep coordination schemes developed for Wireless Sensor Networks, our approach is suitable for Wireless Mesh Networks having much larger traffic demand and non-tree-like routing pattern. In addition, we propose a local duty-cycle control scheme, which regulates node awake time and naturally limits the amount of elastic traffic that moves along energy limited nodes. This is coupled with an implicit admission control scheme, which limits the number of non-elastic flows admitted to the network. More importantly, our scheme does not modify the IEEE 802.11 MAC and does not require information of the traffic demand nor input energy pattern. We have evaluated the performance of our approach using NS3 simulations by considering its traffic volume, lifetime and numerous other parameters and have also compared it to both perfect scheduling and default IEEE 802.11s behavior. Our results are also backed by evaluating numerous randomly generated topologies. A detailed discussion of the effect of topological aspects of the network on its sustainability characteristics is also provided.

Keywords

Wireless mesh networks Energy sustainability Sleep scheduling IEEE 802.11s 

References

  1. 1.
    Akyildiz, I. F., Wang, X., & Wang, W. (2005). Wireless mesh networks: A survey. Computer Networks and ISDN Systems, 47(4), 445–487.CrossRefMATHGoogle Scholar
  2. 2.
    Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 1567–1576.Google Scholar
  3. 3.
    Buettner, M., Yee, G. V., Anderson, E., & Han, R. (2006). X-MAC: A short preamble MAC protocol for duty-cycled wireless sensor networks. In ACM Conference on Embedded Networked Sensor Systems (SenSys), Vol. 4, pp. 307–320.Google Scholar
  4. 4.
    Rhee, I., Warrier, A., Min, J., & Xu, L. (2009). Drand: Distributed randomized TDMA scheduling for wireless ad-hoc networks. IEEE Transactions on Mobile Computing, 8(10), 1384–1396.CrossRefGoogle Scholar
  5. 5.
    Fan, Q., Fan, J., Li, J., & Wang, X. (2012). A multi-hop energy-efficient sleeping MAC protocol based on TDMA scheduling for wireless mesh sensor networks. Journal of Networks, 7(9), 1355–1361.CrossRefGoogle Scholar
  6. 6.
    Dbibih, I., Iala, I., Aboutajdine, D., & Zytoune, O. (2016). ASS-MAC: Adaptive sleeping sensor MAC protocol designed for wireless sensor networks. In International Conference on Information Technology for Organizations Development (IT4OD), pp. 1–5.Google Scholar
  7. 7.
    Nguyen, V. T., Gautier, M., & Berder, O. (2016). FTA-MAC: Fast traffic adaptive energy efficient MAC protocol for wireless sensor networks. In International Conference on Cognitive Radio Oriented Wireless Networks (Crowncom), pp. 207–219.Google Scholar
  8. 8.
    Committee ILS, et al. (1999). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Standard 802(11).Google Scholar
  9. 9.
    Committee S, et al. (2005). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Amendment 8: Medium access control (MAC) quality of service enhancements. IEEE Computer Society.Google Scholar
  10. 10.
    Zhang, F., Todd, T. D., Zhao, D., & Kezys, V. (2006). Power saving access points for IEEE 802.11 wireless network infrastructure. IEEE Transactions on Mobile Computing, 5(2), 144–156.CrossRefGoogle Scholar
  11. 11.
    Li, Y., Todd, T. D., & Zhao, D. (2005). Access point power saving in solar/battery powered IEEE 802.11 ESS mesh networks. In International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks, pp. 44–49.Google Scholar
  12. 12.
    Association IS, et al. (2012). 802.11-2012-IEEE standard for information technology—Telecommunications and information exchange between systems local and metropolitan area networks-specific requirements part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Standard.Google Scholar
  13. 13.
    Afzal, B., Alvi, S. A., & Shah, G. A. (2016). Adaptive duty cycling based multi-hop PSMP for internet of multimedia things. In 13th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp. 895–900.Google Scholar
  14. 14.
    Ghosh, D., Gupta, A., & Mohapatra, P. (2007). Admission control and interference-aware scheduling in multi-hop WIMAX networks. In IEEE International Conference on Mobile Adhoc and Sensor Systems, pp. 1–9.Google Scholar
  15. 15.
    Zou, J., & Zhao, D. (2009). Real-time CBR traffic scheduling in IEEE 802.16-based wireless mesh networks. Wireless Networks, 15(1), 65–72.CrossRefGoogle Scholar
  16. 16.
    Wang, Z., Li, J., Kang, L., Wang, C. & Zhang, Y. (2015). Low-latency tdma sleep scheduling in wireless sensor networks. In IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–6.Google Scholar
  17. 17.
    MalekpourShahraki, M., Barghi, H., Azhari, S. V., & Asaiyan, S. (2016). Distributed and Energy Efficient Scheduling for IEEE802.11s Wireless EDCA Networks. Wireless Personal Communications, 90(1), 301–323.CrossRefGoogle Scholar
  18. 18.
    Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 6(4), 32.CrossRefGoogle Scholar
  19. 19.
    Vigorito, C., Ganesan, D., & Barto, A. (2007). Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In The 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 21–30.Google Scholar
  20. 20.
    Buchli, B., Sutton, F., Beutel, J., & Thiele, L. (2014). Dynamic power management for long-term energy neutral operation of solar energy harvesting systems. In 12th ACM Conference on Embedded Network Sensor Systems (SenSys), pp. 31–45.Google Scholar
  21. 21.
    Peng, S., & Low, C. (2014). Prediction free energy neutral power management for energy harvesting wireless sensor nodes. Ad Hoc Networks, 13, 351–367.CrossRefGoogle Scholar
  22. 22.
    Bui, N., & Rossi, M. (2015). Staying alive: System design for self-sufficient sensor networks. ACM Transactions on Sensor Networks (TOSN), 11(3), 40.CrossRefGoogle Scholar
  23. 23.
    Romaniello, G., Alphand, O., Guizzetti, R., & Duda, A. (2015). Sustainable traffic aware duty-cycle adaptation in harvested multi-hop wireless sensor. In IEEE 81st Vehicular Technology Conference (VTC Spring), pp. 1–6.Google Scholar
  24. 24.
    Cai, L. X., Liu, Y., Luan, T., Shen, X., Mark, J., & Poor, H. V. (2014). Sustainability analysis and resource management for wireless mesh networks with renewable energy supplies. IEEE Journal on Selected Areas in Communications, 32(2), 345–355.CrossRefGoogle Scholar
  25. 25.
    Teng, R., Li, H., Zhang, B., & Miura, R. (2016). Differentiation presentation for sustaining internet access in a disaster-resilient homogeneous wireless infrastructure. IEEE Access, 4, 514–528.CrossRefGoogle Scholar
  26. 26.
    Zhou, L., Kang, G., Zhang, N., & Cheng, J. (2015). Spectral efficiency guaranteed sustainable routing for energy renewable wireless mesh networks. In International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–5.Google Scholar
  27. 27.
    Badawy, G. H., Sayegh, A. A., & Todd, T. D. (2009). Fair flow control in solar powered WLAN mesh networks. In IEEE Wireless Communications and Networking Conference(WCNC), pp. 1–6.Google Scholar
  28. 28.
    Sarkar, S., Khouzani, M. H. R., & Kar, K. (2013). Optimal routing and scheduling in multihop wireless renewable energy networks. IEEE Transaction on Automatic Control, 58(7), 1792–1798.MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Porsch, M., & Bauschert, T. (2014). Aligned beacon transmissions to increase IEEE 802.11s light sleep mode scalability. In Advances in Communication Networking, pp. 173–184.Google Scholar
  30. 30.
    Safonov, A., & Lyakhov, A. (2008). Synchronization and beaconing in IEEE 802.11s mesh networks. In International Conference on Telecommunications (ICT), pp. 1–6.Google Scholar
  31. 31.
    Heusse, M., Rousseau, F., Berger-Sabbatel, G., & Duda, A. (2003). Performance anomaly of 802.11b. In: 22th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), Vol. 2. pp. 836–843.Google Scholar
  32. 32.
    Halperin, D., Greenstein, B., Sheth, A., & Wetherall, D. (2010). Demystifying 802.11n power consumption. In Proceedings of the 2010 International Conference on Power Aware Computing and Systems, p. 1.Google Scholar
  33. 33.
    Wald, L., Albuisson, M., Best, C., Delamare, C., Dumortier, D., Gaboardi, E., et al. (2002). Soda: A project for the integration and exploitation of networked solar radiation databases. In Environmental Communication in the Information Society, International Society for Environmental Protection, Vienna, Austria, pp. 713–720. http://www.soda-is.com.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer EngineeringIran University of Science and TechnologyTehranIran

Personalised recommendations