Advertisement

A Method of Balanced Sleep Scheduling in Renewable Wireless Sensor Networks

  • Maohan Song
  • Weidang Lu
  • Hong Peng
  • Zhijiang Xu
  • Jingyu Hua
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

Energy harvesting from its environmental sources becomes an integral part of green cities. This paper considers a low-energy consumption Wireless Sensor Networks to improve energy utilization in green cities. By this approach, a wireless node can directly harvest energy from its ambient by introducing an energy-harvesting layer on the top of traditional WSN layer. The energy harvesting layer composed of charging points (CPs) that it can harvest energy from ambient renewable energy sources (solar, vibration, light, and electromagnetic wave, etc.) transfer the harvested energy to the underlying WSN layer by wireless energy transfer. Furthermore, in order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. The proposed scheme applies energy informatics to increase the energy efficiency by optimizing energy harvesting time interval and energy consumption of the node for uniform data gathering over the network.

Keywords

Green cities Energy informatics Energy harvesting Duty cycles 

References

  1. 1.
    Cherry, S.: How to build a green city. IEEE Spectr. 44(6), 26–29 (2007)CrossRefGoogle Scholar
  2. 2.
    IEEE Standard for green smart home and residential quarter control network protocol. IEEE Std 1888.4-2016, pp. 1–32, June 2017Google Scholar
  3. 3.
    Zhu, C., Leung, V.C.M., Wang, K., Yang, L.T., Zhang, Y.: Multimethod data delivery for green sensor-cloud. IEEE Commun. Mag. 55(5), 176–182 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhong, W., Yu, R., Xie, S., Zhang, Y., Tsang, D.H.K.: Software defined networking for flexible and green energy internet. IEEE Commun. Mag. 54(12), 68–75 (2016)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Xiong, K., Fan, P., Zhong, Z.: RF energy harvesting wireless powered sensor networks for smart cities. IEEE Access 5, 9348–9358 (2017)CrossRefGoogle Scholar
  6. 6.
    Castagnetti, A., Pegatoquet, A., Le, T.N., Auguin, M.: A joint duty-cycle and transmission power management for energy harvesting WSN. IEEE Trans. Ind. Inform. 10(2), 928–936 (2014)CrossRefGoogle Scholar
  7. 7.
    Djenouri, D., Bagaa, M., Chelli, A., Balasingham, I.: Energy harvesting aware minimum spanning tree for survivable WSN with minimum relay node addition. In: Proceedings of IEEE Globecom Workshops (GC Wkshps), pp. 1–6, December 2016Google Scholar
  8. 8.
    Ju, H., Zhang, R.: Throughput maximization in wireless powered communication networks. IEEE Trans. Wirel. Commun. 13(1), 418–428 (2014)CrossRefGoogle Scholar
  9. 9.
    Fang, W., Mukherjee, M., Shu, L., Zhou, Z., Hancke, G.: Energy utilization concerned sleep scheduling in wireless powered communication networks. In: Proceedings of IEEE ICC, pp. 1–6, May 2017Google Scholar
  10. 10.
    Han, G., Qian, A., Jiang, J., Sun, N., Liu, L.: A grid-based joint routing and charging algorithm for industrial wireless rechargeable sensor networks. Elsevier Comput. Netw. 101, 19–28 (2016)CrossRefGoogle Scholar
  11. 11.
    Suh, Y.-H., Chang, K.: A high-efficiency dual-frequency rectenna for 2.45-and 5.8-GHz wireless power transmission. IEEE Trans. Microw. Theory Tech. 50(7), 1784–1789 (2002)CrossRefGoogle Scholar
  12. 12.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of IEEE 33rd Annual Hawaii International Conference on System Sciences, Maui, Hawaii, pp. 3005–3014 (2000)Google Scholar
  13. 13.
    Bianchi, G., Fratta, L., Oliveri, M.: Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs. In: Proceedings of IEEE PIMRC, pp. 392–396, October 1996Google Scholar
  14. 14.
    Zhu, C., Chen, Y., Wang, L., Shu, L., Zhang, Y.: SMAC-based proportional fairness backoff scheme in wireless sensor networks. In: Proceedings of ACM 6th IWCMC, June/July 2010, pp. 138–142 (2010)Google Scholar
  15. 15.
    Nath, S., Gibbons, P.B.: Communicating via fireflies: Geographic routing on duty-cycled sensors. In: Proceedings of IEEE/ACM 6th IPSN, Cambridge, MA, pp. 440–449 (2007)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Maohan Song
    • 1
  • Weidang Lu
    • 1
  • Hong Peng
    • 1
  • Zhijiang Xu
    • 1
  • Jingyu Hua
    • 1
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouPeople’s Republic of China

Personalised recommendations