Skip to main content

Advertisement

Log in

Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

A Correction to this article was published on 11 February 2021

This article has been updated

Abstract

Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.

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

Similar content being viewed by others

Change history

References

  1. Kurs A, Karalis A, Moffatt R, Joannopoulos J, Fisher P, Soljacic M (2007) Wireless power transfer via strongly coupled magnetic resonances. Science 317(5834):83–86. https://doi.org/10.1126/science.1143254

    Article  MathSciNet  Google Scholar 

  2. Lu X, Wang P, Niyato D, Kim D, Han Z (2016) Wireless charging technologies: fundamentals, standards, and network applications. IEEE Communications Surveys & Tutorials 18(2):1413–1452. https://doi.org/10.1109/comst.2015.2499783

    Article  Google Scholar 

  3. Akan O, Cetinkaya O, Koca C, Ozger M (2018) Internet of hybrid energy harvesting things. IEEE Internet Things J 5(2):736–746. https://doi.org/10.1109/jiot.2017.2742663

    Article  Google Scholar 

  4. Tong B, Wang G, Zhang W, Wang C (2011) Node reclamation and replacement for long-lived sensor networks. IEEE Transactions On Parallel And Distributed Systems 22(9):1550–1563. https://doi.org/10.1109/tpds.2011.25

    Article  Google Scholar 

  5. Liang W, Ren X, Jia X, Xu X (2013) Monitoring quality maximization through fair rate allocation in harvesting sensor networks. IEEE Transactions On Parallel And Distributed Systems 24(9):1827–1840. https://doi.org/10.1109/tpds.2013.136

    Article  Google Scholar 

  6. Jonah O, Georgakopoulos S (2013) Wireless power transfer in concrete via strongly coupled magnetic resonance. IEEE Trans Antennas Propag 61(3):1378–1384. https://doi.org/10.1109/tap.2012.2227924

    Article  Google Scholar 

  7. Ma Y, Liang W, Xu W (2018) Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously. IEEE/ACM Trans Networking 26(4):1591–1604. https://doi.org/10.1109/tnet.2018.2841420

    Article  Google Scholar 

  8. He L, Zhuang Y, Pan J, Xu J (2010) Evaluating on-demand data collection with mobile elements in wireless sensor networks. In: IEEE 72nd Vehicular Technology Conference - Fall, Ottawa, ON, 2010, pp. 1–5, https://doi.org/10.1109/VETECF.2010.5594515

  9. He L, Kong L, Gu Y, Pan J, Zhu T (2015) Evaluating the on-demand Mobile charging in wireless sensor networks. IEEE Trans Mob Comput 14(9):1861–1875. https://doi.org/10.1109/tmc.2014.2368557

    Article  Google Scholar 

  10. Shi Y, Xie L, Hou YT, Sherali HD (2011) On renewable sensor networks with wireless energy transfer. In: Proc. IEEE INFOCOM, pp. 1350–1358

  11. Peng Y, Li Z, Zhang W, Qiao D (2010) Prolonging sensor network lifetime through wireless charging. In: Proc. IEEE Real-Time Syst. Symp., Nov. 2010, pp. 129–139

  12. Guo S, Wang C, Yang Y (2013) Mobile data gathering with wireless energy replenishment in rechargeable sensor networks. In: Proc. IEEE Int.Conf. Comput. Commun., Apr. 2013, pp. 1932–1940

  13. Tomar A, Jana PK (2017) Designing energy efficient traveling paths for multiple mobile chargers in wireless rechargeable sensor networks. In: Proc. 10th Int. Conf. Contemp. Comput. (IC3), Aug. 2017, pp. 1–6

  14. Kaswan A, Tomar A, Jana PK (2018) An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks. J Netw Comput Appl 114(15):123–134

    Article  Google Scholar 

  15. Xu W, Liang W, Jia X, Xu Z, Li Z, Liu Y (2018) Maximizing sensor lifetime with the minimal service cost of a mobile charger in wireless sensor networks. IEEE Trans Mobile Comput 17(11):2564–2577

    Article  Google Scholar 

  16. Lin C, Wei S, Deng J, Obaidat MS, Song H, Wang L, Wu G (2018) GTCCS: a game theoretical collaborative charging scheduling for on demand charging architecture. IEEE Trans Veh Technol 67(12):12124–12136

    Article  Google Scholar 

  17. Lyu Z, Wei Z, Pan J, Chen H, Xia C, Han J, Shi L (2019) Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm. Appl Soft Comput 75(1):388–403

    Article  Google Scholar 

  18. Liu K, Peng J, He L, Pan J, Li S, Ling M, Huang Z (2019) An active mobile charging and data collection scheme for clustered sensor networks. IEEE Trans Veh Technol 68(5):5100–5113

    Article  Google Scholar 

  19. Lyu Z, Wei Z, Lu Y, Wang X, Li M, Xia C, Han J (2019) Multi-node charging planning algorithm with an energy-limited WCE in WRSNs. IEEE Access 7:47154–47170

    Article  Google Scholar 

  20. Tomar A, Muduli L, Jana PK (2019) An efficient scheduling scheme for on-demand mobile charging in wireless rechargeable sensor networks. Pervasive and Mobile Computing, Volume 59, 101074, ISSN 1574-1192

  21. He S, Chen J, Jiang F, Yau DKY, Xing G, Sun Y (2013) Energy provisioning in wireless rechargeable sensor networks. IEEE Trans. Mobile Comput. 12(10):1931–1942

    Article  Google Scholar 

  22. Shu Y, Yousefi H, Cheng P, Chen J, Gu YJ, He T, Shin KG (2016) Near-optimal velocity control for mobile charging in wireless rechargeable sensor networks. IEEE Trans. Mobile Comput. 15(7):1699–1713

    Article  Google Scholar 

  23. Hoang DC, Kumar R, Panda SK (2010) Fuzzy C-means clustering protocol for wireless sensor networks. IEEE International Symposium on Industrial Electronics, Bari, pp. 3477–3482, https://doi.org/10.1109/ISIE.2010.5637779

  24. Gupta GP, Jha S (2018) Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Eng Appl Artif Intell 68:101–109

    Article  Google Scholar 

  25. Valian E, Valian, E (2012) A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems, Eng Optim, 1–14

  26. Yang X-S, Deb S (2013) Cuckoo search: recent advances and applications,” Neural Comput & Applic, 1–6

Download references

Acknowledgments

This work was supported by Seed research Grant Project (NITRR/Seed Grant/2016-17/21) by the NIT, Raipur.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Govind P. Gupta.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chawra, V.K., Gupta, G.P. Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks. Peer-to-Peer Netw. Appl. 14, 1303–1315 (2021). https://doi.org/10.1007/s12083-020-01052-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-01052-8

Keywords

Navigation