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Solving the Multiple Charging Vehicles Scheduling Problem for Wireless Rechargeable Sensor Networks Using Cuckoo Search Approach

  • Rei-Heng Cheng
  • Shang-Kuan Chen
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 110)

Abstract

Wireless rechargeable sensor networks (WRSNs) get the focus of attention recently due to the rapid progress in wireless charging technology. Since the loading of each sensor is different, sensors request for charging in different frequencies. Also, sensors may deplete their energy quickly and need to be charged urgently under some circumstances. Therefore, a good charging route should not only minimize the moving distance of the charging device to save its energy but also charge all the sensors in time to keep the entire network working properly. In this paper, a cuckoo search approach is proposed to solve this complex problem. Based on the K-center concept, all the recharging tasks are divided into groups according to the location of sensors waiting to be charged. Preliminary simulation results show that the pre-grouping strategy can further improve the performance of the proposed cuckoo search approach.

Keywords

Wireless recharging sensor networks Cuckoo search approach Charging scheduling K-center 

References

  1. 1.
    Kurs, A.B., Karalis, A., Moffatt, R., Joannopoulos, J.D., Fisher, P.H., Soljacic, M.: Wireless power transfer via strongly coupled magnetic resonances. Science 317, 83–86 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Karalis, A., Joannopoulos, J.D., Soljacic, M.: Efficient wireless non-radiative mid-range energy transfer. Ann. Phys. 323, 34–48 (2008)CrossRefGoogle Scholar
  3. 3.
    Barman, S.D., Reza, A.W., Kumar, N., Karim, M.E., Munir, A.B.: Wireless powering by magnetic resonant coupling: recent trends in wireless power transfer system and its applications. Renew. Sustain. Energy Rev. 51, 1525–1552 (2015)CrossRefGoogle Scholar
  4. 4.
    Park, C., Chou, P.: AmbiMax: autonomous energy harvesting platform for multi-supply wireless sensor nodes. In: 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, pp. 168–177. IEEE, Reston (2006)Google Scholar
  5. 5.
    Jiang, X., Polastre, J., Culler, D.: Perpetual environmentally powered sensor networks. In: 4th International Symposium on Information Processing in Sensor Networks, pp. 463–468. IEEE, Boise (2005)Google Scholar
  6. 6.
    Lin, K., Yu, J., Hsu, J., Zahedi, S., Lee, D., Friedman, J., Kansal, A., Raghunathan, V., Srivastava, M.: Heliomote: enabling long-lived sensor networks through solar energy harvesting. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 309–309. ACM, San Diego (2005)Google Scholar
  7. 7.
    Lin, T.S., Weng, C.C.: Using a quadratic Gaussian function to describe the accumulated charging energy of a lithium-ion battery. Hwa Kang J. Eng. 27, 141–147 (2011)Google Scholar
  8. 8.
    Beigel, R., Wu, J., Zheng, H.: On optimal scheduling of multiple mobile chargers in wireless sensor networks. In: Proceedings of the First International Workshop on Mobile Sensing, Computing and Communication, pp. 1–6. ACM, Pennsylvania (2014)Google Scholar
  9. 9.
    Liao, J.-H., Hong, C.-M., Jiang, J.-R.: An adaptive algorithm for charger deployment optimization in wireless rechargeable sensor networks. In: International Computer Symposium, pp. 2080–2089, IOS Press, Taichung (2014)Google Scholar
  10. 10.
    Pan, M., Li, H., Pang, Y., Yu, R., Lu, Z., Li, W.: Optimal energy replenishment and data collection in wireless rechargeable sensor networks. In: Global Communications Conference, pp. 125–130. IEEE, Austin (2014)Google Scholar
  11. 11.
    Chen, S., Sinha, P., Shroff, N.B., Joo, C.: A simple asymptotically optimal energy allocation and routing scheme in rechargeable sensor networks. In: Proceedings IEEE INFOCOM, pp. 379–387, IEEE, Orlando (2012)Google Scholar
  12. 12.
    Madhja, A., Nikoletseas, S., Raptis, T.P.: Distributed wireless power transfer in sensor networks with multiple mobile chargers. Comput. Netw. 80, 89–108 (2015)CrossRefGoogle Scholar
  13. 13.
    Dai, H., Wu, X., Chen, G., Xu, L., Lin, S.: Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks. Comput. Commun. 46, 54–65 (2014)CrossRefGoogle Scholar
  14. 14.
    Xu, C., Cheng. R.-H., Wu, T.K.: Wireless rechargeable sensor networks with separable charger array. Int. J. Distrib. Sens. Netw. 14(4), (2018).  https://doi.org/10.1177/1550147718768990CrossRefGoogle Scholar
  15. 15.
    Zhao, M., Li, J., Yang, Y.: A framework of joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks. IEEE Trans. Mob. Comput. 13(12), 2689–2705 (2014)CrossRefGoogle Scholar
  16. 16.
    Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214. IEEE, Coimbatore (2009)Google Scholar
  17. 17.
    Mareli, M., Twala, B.: An adaptive Cuckoo search algorithm for optimization. Appl. Comput. Inform. 14(2), 107–115 (2018)CrossRefGoogle Scholar
  18. 18.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Engineering CollegeYango UniversityFuzhouChina

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