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

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Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 110))

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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.

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References

  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)

    Article  MathSciNet  Google Scholar 

  2. Karalis, A., Joannopoulos, J.D., Soljacic, M.: Efficient wireless non-radiative mid-range energy transfer. Ann. Phys. 323, 34–48 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. Madhja, A., Nikoletseas, S., Raptis, T.P.: Distributed wireless power transfer in sensor networks with multiple mobile chargers. Comput. Netw. 80, 89–108 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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/10.1177/1550147718768990

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Mareli, M., Twala, B.: An adaptive Cuckoo search algorithm for optimization. Appl. Comput. Inform. 14(2), 107–115 (2018)

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Information Engineering College, Yango University, Fuzhou, 350015, China

    Rei-Heng Cheng & Shang-Kuan Chen

Authors
  1. Rei-Heng Cheng
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  2. Shang-Kuan Chen
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Corresponding author

Correspondence to Shang-Kuan Chen .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan

    Akinori Ito

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Pei-Wei Tsai

  4. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Cheng, RH., Chen, SK. (2019). Solving the Multiple Charging Vehicles Scheduling Problem for Wireless Rechargeable Sensor Networks Using Cuckoo Search Approach. In: Pan, JS., Ito, A., Tsai, PW., Jain, L. (eds) Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol 110. Springer, Cham. https://doi.org/10.1007/978-3-030-03748-2_4

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