Skip to main content
Log in

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Wireless rechargeable sensor networks (WRSNs) are a popular and promising field of research that can be used in many different fields. The battery life and storage space of the sensors are limited, due to this it is hard to keep the network up for longer. Combining wireless energy transfer and wireless data gathering devices on a Mobile Vehicle (MV) is one solution to this challenge. The objective of this work is to reduce the number of dead sensors and packet delivery delay. We proposed the circle-covering based algorithm to determine sojourn point based on energy consumption rates and the location of sensors. An Improved Grey Wolf Optimization (IGWO) meta-heuristic algorithm partitions the network into the minimum number of regions, assigns a MV to each region, and ensures balanced sub-tour lengths among the regions. A novel weight function is proposed to determine the order of the sojourn points. To accomplish our objectives, we propose a heuristic partial charging and data gathering strategy to determine the sojourn times of the MVs at the sojourn points. The performance of our proposed PCDGS scheme is compared with IMPSS, PMCDC, MOAC and JERDC schemes. The simulation results show that our proposed PCDGS scheme outperforms the others.

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
Algorithm 1
Fig. 6
Fig. 7
Algorithm 2
Fig. 8
Algorithm 3
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Data availability

In this work, the standard datasets are not used. The data generated and analyzed during the current study are available in the experiment section.

References

  1. Othman, M.F., Shazali, K.: Wireless sensor network applications: a study in environment monitoring system. Procedia Eng. 41, 1204–1210 (2012)

    Article  Google Scholar 

  2. Ahmad Ali, Yu., Ming, S.C., Iram, S.: A comprehensive survey on real-time applications of wsn. Future Internet 9(4), 77 (2017)

    Article  Google Scholar 

  3. Wen, W., Zhao, S., Shang, C., Chang, C.-Y.: Eapc: energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sens. J. 18(2), 890–901 (2017)

    Article  Google Scholar 

  4. Zhang, L., Ansari, N.: Latency-aware iot service provisioning in uav-aided mobile-edge computing networks. IEEE Internet Things J. 7(10), 10573–10580 (2020)

    Article  Google Scholar 

  5. Baek, J., Han, S.I., Han, Y.: Optimal uav route in wireless charging sensor networks. IEEE Internet Things J. 7(2), 1327–1335 (2019)

    Article  Google Scholar 

  6. Cammarano, A., Petrioli, C., Spenza, D.: Online energy harvesting prediction in environmentally powered wireless sensor networks. IEEE Sens. J. 16(17), 6793–6804 (2016)

    Article  Google Scholar 

  7. Adu-Manu, K.S., Adam, N., Tapparello, C., Ayatollahi, H., Heinzelman, W.: Energy-harvesting wireless sensor networks (eh-wsns): a review. ACM Trans. Sens. Netw. 14(2), 10 (2018)

    Article  Google Scholar 

  8. Mazunga, F., Nechibvute, A.: Ultra-low power techniques in energy harvesting wireless sensor networks: recent advances and issues. Sci. Afr. 11, e00720 (2021)

    Google Scholar 

  9. Dash, D.: Geometric algorithm for finding time-sensitive data gathering path in energy harvesting sensor networks. IEEE Trans. Intell. Transp. Syst. 23(7), 7547–7556 (2021)

    Article  Google Scholar 

  10. Liu, X., Ansari, N.: Toward green iot: Energy solutions and key challenges. IEEE Commun. Mag. 57(3), 104–110 (2019)

    Article  Google Scholar 

  11. Zhang, Z., Pang, H., Georgiadis, A., Cecati, C.: Wireless power transfer-an overview. IEEE Trans. Ind. Electron. 66(2), 1044–1058 (2018)

    Article  Google Scholar 

  12. La Rosa, R., Livreri, P., Trigona, C., Di Donato, L., Sorbello, G.: Strategies and techniques for powering wireless sensor nodes through energy harvesting and wireless power transfer. Sensors 19(12), 2660 (2019)

    Article  Google Scholar 

  13. Liang, W., Zichuan, X., Wenzheng, X., Shi, J., Mao, G., Das, S.K.: Approximation algorithms for charging reward maximization in rechargeable sensor networks via a mobile charger. ACM Trans. Netw. 25(5), 3161–3174 (2017)

    Article  Google Scholar 

  14. Wang, K., Wang, L., Obaidat, M.S., Lin, C., Alam, M.: Extending network lifetime for wireless rechargeable sensor network systems through partial charge. IEEE Syst. J. 15(1), 1307–1317 (2020)

    Article  Google Scholar 

  15. Wang, C., Li, J., Yang, Y., Ye, F.: Combining solar energy harvesting with wireless charging for hybrid wireless sensor networks. IEEE Trans. Mob. Comput. 17(3), 560–576 (2017)

    Article  Google Scholar 

  16. Liu, B.-H., Nguyen, N.-T., Pham, V.-T., Lin, Y.-X.: Novel methods for energy charging and data collection in wireless rechargeable sensor networks. Int. J. Commun. Syst. 30(5), e3050 (2017)

    Article  Google Scholar 

  17. Boukerche, A., Qiyue, W., Sun, P.: A novel joint optimization method based on mobile data collection for wireless rechargeable sensor networks. IEEE Trans. Green Commun. Netw. 5(3), 1610–1622 (2021)

    Article  Google Scholar 

  18. Guo, S., Wang, C., Yang, Y.: Mobile data gathering with wireless energy replenishment in rechargeable sensor networks. In: 2013 Proceedings IEEE INFOCOM, pp. 1932–1940. Turin, Italy, IEEE (2013)

  19. 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 

  20. Idrees, A.K., Couturier, R.: Energy-saving distributed monitoring-based firefly algorithm in wireless sensors networks. J. Supercomput. 78(2), 2072–2097 (2022)

    Article  Google Scholar 

  21. Liang, J., Tian, M., Liu, Y., Zhou, J.: Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Sci. Rep. 12(1), 11687 (2022)

    Article  Google Scholar 

  22. Wang, J., Liu, Y., Rao, S., Zhou, X., Jinbin, H.: A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks. Ad Hoc Netw. 150, 103284 (2023)

    Article  Google Scholar 

  23. Idrees, A.K., Alhussaini, R., Salman, M.A.: Energy-efficient two-layer data transmission reduction protocol in periodic sensor networks of iots. Pers. Ubiquit. Comput. 27(2), 139–158 (2023)

    Article  Google Scholar 

  24. Balaji, S., Pavithra, R., Arivudainambi, D., Varunkumar, K.A., Suresh, A., Omar, Marwan Marwan, Bashir, Ali Kashif: Towards efficient sensor deployment in internet of things for target coverage and sensor connectivity. IEEE Transactions on Consumer Electronics (2023)

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

    Article  Google Scholar 

  26. Chen, Y., Jiao, W., Wenrui, Yu.: The combined strategy of energy replenishment and data collection in heterogenous wireless rechargeable sensor networks. IEEE Syst. J. 17(3), 1–12 (2022)

    Google Scholar 

  27. Jiao, W., Tian, M., Yun, X.: A combining strategy of energy replenishment and data collection in wireless sensor networks. IEEE Sens. J. 22(7), 7411–7426 (2022)

    Article  Google Scholar 

  28. Baek, J., Han, S.I., Han, Y.: Optimal uav route in wireless charging sensor networks. IEEE Internet Things J. 7(2), 1327–1335 (2020)

    Article  Google Scholar 

  29. Zhang, M., Cai, W.: Data collecting and energy charging oriented mobile path design for rechargeable wireless sensor networks. J. Sens. (2022). https://doi.org/10.1155/2022/5004507

    Article  Google Scholar 

  30. Anwit, R., Jana, P.K., Tomar, A.: Sustainable and optimized data collection via mobile edge computing for disjoint wireless sensor networks. IEEE Trans. Sustain. Comput. 7(2), 471–484 (2021)

    Article  Google Scholar 

  31. Han, G., Yang, X., Liu, L., Zhang, W.: A joint energy replenishment and data collection algorithm in wireless rechargeable sensor networks. IEEE Internet Things J. 5(4), 2596–2604 (2018)

    Article  Google Scholar 

  32. Wei, Z., Xia, C., Yuan, X., Sun, R., Lyu, Z., Shi, L., Ji, J.: The path planning scheme for joint charging and data collection in wrsns: A multi-objective optimization method. J. Netw. Comput. Appl. 156, 102565 (2020)

    Article  Google Scholar 

  33. Lyu, Z., Zhenchun Wei, X., Wang, Y.F., Xia, C., Shi, L.: A periodic multinode charging and data collection scheme with optimal traveling path in wrsns. IEEE Syst. J. 14(3), 3518–3529 (2020)

    Article  Google Scholar 

  34. Yadav, C.B.K., Dash, D.: An energy efficient periodic data gathering and charging schedule using mvs in wireless rechargeable sensor networks. Computing 105(11), 2563–2593 (2023)

    Article  MathSciNet  Google Scholar 

  35. Das, R., Dash, D.: Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network. Int. J. Commun. Syst. 36(15), e5573 (2023)

    Article  Google Scholar 

  36. Yadav, C.B., Kumar, Dash, D.: A novel data collection and partial charging scheme using multiple mobile vehicles in wireless rechargeable sensor networks. Arab. J. Sci. Eng. (2024). https://doi.org/10.1007/s13369-023-08687-8

    Article  Google Scholar 

  37. He, S., Chen, J., Jiang, F., Yau, D.K.Y., Xing, G., Sun, Y.: Energy provisioning in wireless rechargeable sensor networks. IEEE Trans. Mob. Comput. 12(10), 1931–1942 (2012)

    Article  Google Scholar 

  38. Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S.: An improved grey wolf optimizer for solving engineering problems. Expert Syst. Appl. 166, 113917 (2021)

    Article  Google Scholar 

  39. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  40. He, L., Linghe Kong, Y.G., Pan, J., Zhu, T.: Evaluating the on-demand mobile charging in wireless sensor networks. IEEE Trans. Mob. Comput. 14(9), 1861–1875 (2014)

    Article  Google Scholar 

  41. Tomar, A., Muduli, L., Jana, P.K.: An efficient scheduling scheme for on-demand mobile charging in wireless rechargeable sensor networks. Pervasive Mob. Comput. 59, 101074 (2019)

    Article  Google Scholar 

  42. Dong, Y., Wang, Y., Li, S., Cui, M., Hao, W.: Demand-based charging strategy for wireless rechargeable sensor networks. ETRI J. 41(3), 326–336 (2019)

    Article  Google Scholar 

  43. Liang, W., Wenzheng, X., Ren, X., Jia, X., Lin, X.: Maintaining large-scale rechargeable sensor networks perpetually via multiple mobile charging vehicles. ACM Trans. Sens. Netw. 12(2), 1–26 (2016)

    Article  Google Scholar 

  44. Liu, T., Wu, B., Zhang, S., Peng, J., Xu, W.: An effective multi-node charging scheme for wireless rechargeable sensor networks. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 2026–2035. Toronto, ON, Canada, IEEE (2020)

  45. Ma, Yu., Liang, W., Wenzheng, X.: Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously. ACM Trans. Netw. 26(4), 1591–1604 (2018)

    Article  Google Scholar 

  46. Xie, L., Shi, Y., Hou, Y.T., Lou, W., Sherali, H.D., Midkiff, S.F.: Multi-node wireless energy charging in sensor networks. IEEE/ACM Trans. Netw. 23(2), 437–450 (2014)

    Article  Google Scholar 

  47. Ostertagova, E., Ostertag, O.: Methodology and application of one-way ANOVA. Am. J. Mech. Eng. 1, 256–261 (2013)

    Google Scholar 

Download references

Acknowledgements

We like to thank the anonymous reviewers for their valuable comments.

Funding

This work is supported by the Science and Engineering Research Board, a statutory body of the Department of Science and Technology (DST), Govt. of India [Grant number: CRG/2023/001572].

Author information

Authors and Affiliations

Authors

Contributions

CBKY: methodology, coding, writing. DD: writing—review & editing, formal analysis.

Corresponding author

Correspondence to Dinesh Dash.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, C.B.K., Dash, D. An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04425-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04425-3

Keywords

Navigation