Skip to main content

Advertisement

Log in

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

  • Published:
Wireless Networks Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33

Similar content being viewed by others

Notes

  1. Synchronization is out the scope of this work but can be potentially achieved using a number of schemes, such as [29, 30]

  2. As an example, it can be determined such that MAPs are able to sustain all critical and control traffic until the beginning of the next daylight period. Examples of critical traffic is video monitoring of an important region or emergency messages. An example for calculating \(B^{req}\) is described in the “Appendix”.

  3. Small variations in solar input energy over the sustainability epoch are averaged out to obtain a constant expected input energy for each epoch.

  4. In practice the \(B^{req}\) profile is computed based on traffic demand during night-time. This is out of the scope of this manuscript. An example derivation is provided in the Annex.

  5. Shorter delay can be easily achieved by properly scaling the service interval.

References

  1. Akyildiz, I. F., Wang, X., & Wang, W. (2005). Wireless mesh networks: A survey. Computer Networks and ISDN Systems, 47(4), 445–487.

    MATH  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  8. Committee ILS, et al. (1999). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Standard 802(11).

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  21. Peng, S., & Low, C. (2014). Prediction free energy neutral power management for energy harvesting wireless sensor nodes. Ad Hoc Networks, 13, 351–367.

    Article  Google Scholar 

  22. Bui, N., & Rossi, M. (2015). Staying alive: System design for self-sufficient sensor networks. ACM Transactions on Sensor Networks (TOSN), 11(3), 40.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

  30. Safonov, A., & Lyakhov, A. (2008). Synchronization and beaconing in IEEE 802.11s mesh networks. In International Conference on Telecommunications (ICT), pp. 1–6.

  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.

  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.

  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.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyed Vahid Azhari.

Appendix: Example of required battery calculation

Appendix: Example of required battery calculation

Recall that, \(B^{req}\) is defined as the nominal minimum battery charge that should exist at the beginning of a given sustainability epoch. It can be determined based on any policy desired and according to any method, depending on the network design and operation objective. We treat \(B^{req}\) as a requirement that should be met for the sustainability of the WMN, and one which is set outside the framework presented by our sleep coordination approach. Hence, the determination of \(B^{req}\) is out of the scope of our work. For the sake of illustration, however, we include one such policy and calculation method.

Fig. 34
figure 34

BM + critical connections and input energy profile

Fig. 35
figure 35

Total input energy, total required energy for BM and critical connections and minimum required battery for energy sustainability

The example policy is to maintain \(B^{req}\) enough for management and control traffic as well as some amount of critical traffic determined by the network administrator. Hence we have a minimum traffic profile which is translated into the amount of energy required to forward it at any MAP, along with an input energy profile which is based on historical irradiation data, all shown in Fig. 34. This information is then used according to the methodology presented in Eq. (21) to derive \(B^{req}\). Figure 35 shows the minimum battery requirement for serving critical and control traffic until next daylight, which is calculated as follows. Let \(P_{\min}(t)\) be the minimum power required by an MAP for its critical and control traffic. Here it is not assumed that an MAP is always awake. The power requirement of an MAP is calculated based on the amount of traffic it forwards, which translates into a certain activity time, hence energy consumption. To do this, the average power consumption in active mode is used. Take T as the end of current sustainability epoch and \(T_{ND}\) as start time of the next daylight. If \(\varGamma (t)\) is the harvested power, then the minimum required battery at the end of T is

$$B^{req}=\left[ \int _{T}^{T_{ND}}P_{\min}(t) dt-\int _{T}^{T_{ND}}\varGamma (t) dt \right] ^+ ,$$
(21)

Where \([x]^+\) is equal to x if \(x\ge 0\) and 0 if \(x\le 0\). Maintaining this \(B^{req}\) will then ensure that the critical and control traffic is served until next daylight period.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barghi, H., Azhari, S.V. A practical sleep coordination and management scheme with duty cycle control for energy sustainable IEEE 802.11s wireless mesh networks. Wireless Netw 25, 2511–2536 (2019). https://doi.org/10.1007/s11276-018-1683-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-018-1683-6

Keywords

Navigation