Skip to main content
Log in

Research on UAV-Aided WSNs Node Positioning Task Planning in Field Environment

  • Research
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, we present a strategy for node positioning in wireless sensor networks (WSNs) that uses unmanned aerial vehicles (UAVs) as auxiliary devices. Our strategy aims to overcome the high energy consumption, high cost, and low efficiency of traditional methods in field environments. First, we establish a communication coverage model for UAVs and determine the UAV positioning task points by dividing the task area. We further derive the UAV-aided WSN node positioning model. Then, according to the timeliness requirements of the task, we design single UAV and UAV group-aided WSNs positioning strategies. We analyze the UAV group formation change methods, establish optimization models based on positioning task efficiency and UAV energy efficiency, and then determine the optimal UAV formation change schemes for different situations. Finally, we propose a UAV group task planning algorithm, based on virtual trajectory and optimal formation change combination. We verify the feasibility of the proposed positioning method through actual experiments and test the effectiveness of the UAV task allocation strategy through simulation experiments.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Algorithm 1
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38

Similar content being viewed by others

Data availibility

Data will be made available on request.

Code availability

Code will be made available on request.

References

  1. Fascista, A. (2022). Toward integrated large-scale environmental monitoring using WSN/UAV/Crowdsensing: A review of applications, signal processing, and future perspectives. Sensors. https://doi.org/10.3390/s22051824

    Article  Google Scholar 

  2. Basha, A. R. (2022). A review on wireless sensor networks: Routing. Wireless Personal Communications, 125(1), 897–937. https://doi.org/10.1007/s11277-022-09583-4

    Article  Google Scholar 

  3. Thakur, D., Kumar, Y., Kumar, A., & Singh, P. K. (2019). Applicability of wireless sensor networks in precision agriculture: A review. Wireless Personal Communications, 107(1), 471–512. https://doi.org/10.1007/s11277-019-06285-2

    Article  Google Scholar 

  4. Cii, S., Tomasini, G., Bacci, M. L., & Tarsitano, D. (2022). Solar wireless sensor nodes for condition monitoring of freight trains. IEEE Transactions on Intelligent Transportation Systems, 23(5), 3995–4007. https://doi.org/10.1109/tits.2020.3038319

    Article  Google Scholar 

  5. Osamy, W., Khedr, A. M., Salim, A., AlAli, A. I., & El-Sawy, A. A. (2022). Recent studies utilizing artificial intelligence techniques for solving data collection, aggregation and dissemination challenges in wireless sensor networks: A review. Electronics. https://doi.org/10.3390/electronics11030313

    Article  Google Scholar 

  6. Osamy, W., Khedr, A. M., Salim, A., Al Ali, A. I., & El-Sawy, A. A. (2022). Coverage, deployment and localization challenges in wireless sensor networks based on artificial intelligence techniques: A review. IEEE Access, 10, 30232–30257. https://doi.org/10.1109/access.2022.3156729

    Article  Google Scholar 

  7. Sabale, K., & Mini, S. (2019). Anchor node path planning for localization in wireless sensor networks. Wireless Networks, 25(1), 49–61. https://doi.org/10.1007/s11276-017-1538-6

    Article  Google Scholar 

  8. Ojha, T., Misra, S., & Obaidat, M. S. (2020). Seal: Self-adaptive AUV-based localization for sparsely deployed underwater sensor networks. Computer Communications, 154, 204–215. https://doi.org/10.1016/j.comcom.2020.02.050

    Article  Google Scholar 

  9. Huang, S.-P., Chen, C.-B., Wei, T.-Z., Tsai, W.-T., Liou, C.-Y., Mao, Y.-M., Sheng, W.-H., & Mao, S.-G. (2023). Range-extension algorithms and strategies for tdoa ultra-wideband positioning system. Sensors. https://doi.org/10.3390/s23063088

    Article  Google Scholar 

  10. Nemer, I., Sheltami, T., Shakshuki, E., Abu Elkhail, A., & Adam, M. (2021). Performance evaluation of range-free localization algorithms for wireless sensor networks. Personal and Ubiquitous Computing, 25(1), 177–203. https://doi.org/10.1007/s00779-020-01370-x

    Article  Google Scholar 

  11. Liu, W., Yu, H., Zhu, H., & Fang, H. (2023). Research on wireless sensor network localization based on an improved whale optimization algorithm. Journal of Internet Technology, 24(1), 55–64. https://doi.org/10.53106/160792642023012401006

    Article  Google Scholar 

  12. Li, X., Wu, Z., Shen, Z., Xu, Z., Li, X., Li, S., & Han, J. (2023). An indoor and outdoor seamless positioning system for low-cost UGV using PPP/INS/UWB tightly coupled integration. IEEE Sensors Journal, 23(20), 24895–24906. https://doi.org/10.1109/jsen.2023.3310480

    Article  Google Scholar 

  13. Malathy, E. M., Asaithambi, M., Dheeraj, A., & Arputharaj, K. (2022). Hybrid bird swarm optimized quasi affine algorithm based node location in wireless sensor networks. Wireless Personal Communications, 122(2), 947–962. https://doi.org/10.1007/s11277-021-08934-x

    Article  Google Scholar 

  14. Messous, S., Liouane, H., Cheikhrouhou, O., & Hamam, H. (2021). Improved recursive DV-hop localization algorithm with RSSI measurement for wireless sensor networks. Sensors. https://doi.org/10.3390/s21124152

    Article  Google Scholar 

  15. Alrizq, M., Stalin, S., Alyami, S., Roy, V., Mishra, A., Chandanan, A. K., Awad, N. A., & Venkatesh, P. (2023). Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network. Wireless Networks. https://doi.org/10.1007/s11276-023-03469-4

    Article  Google Scholar 

  16. Niculescu, V., Palossi, D., Magno, M., & Benini, L. (2022). Fly, wake-up, find: UAV-based energy-efficient localization for distributed sensor nodes. Sustainable Computing-Informatics & Systems. https://doi.org/10.1016/j.suscom.2022.100666

    Article  Google Scholar 

  17. Niculescu, V., Palossi, D., Magno, M., & Benini, L. (2023). Energy-efficient, precise UWB-based 3-d localization of sensor nodes with a nano-UAV. IEEE Internet of Things Journal, 10(7), 5760–5777. https://doi.org/10.1109/jiot.2022.3166651

    Article  Google Scholar 

  18. Yang, J., Yan, T., & Sun, W. (2023). Polynomial fitting and interpolation method in tdoa estimation of sensors network. IEEE Sensors Journal, 23(4), 3837–3847. https://doi.org/10.1109/jsen.2022.3232625

    Article  Google Scholar 

  19. Chen, L., Gao, Z., Xu, Q., Yang, C., & Li, Y. (2023). Comprehensive evaluation of robust and tight integration of UWB and low-cost IMU. IEEE Sensors Journal, 23(21), 26411–26422. https://doi.org/10.1109/jsen.2023.3309623

    Article  Google Scholar 

  20. Liu, Y., Wang, Y., Shen, X., Wang, J., & Shen, Y. (2021). Uav-aided relative localization of terminals. IEEE Internet of Things Journal, 8(16), 12999–13013. https://doi.org/10.1109/jiot.2021.3064216

    Article  Google Scholar 

  21. Ginanjar, R. R., & Kim, D. -S. (2019) IEEE: Real-time SLFN-based node localization using UAV. In IEEE international conference on industrial cyber physical systems (ICPS) (pp. 101–106). https://doi.org/10.1109/icphys.2019.8780266 .<Go to ISI>://WOS:000518988100013.

  22. . Sallouha, H., Azari, M. M., & Pollin, S. (2018). Energy-constrained UAV trajectory design for ground node localization. In 2018 IEEE global communications conference (pp. 7–7). https://doi.org/10.1109/glocom.2018.8647530 . Times Cited: 0 Globecom Conference Paper GLOBECOM 2018 - 2018 IEEE Global Communications Conference 9-13 Dec. 2018 Abu Dhabi, United Arab Emirates.<Go to ISI>://INSPEC:18472557.

  23. Kouroshnezhad, S., Peiravi, A., Haghighi, M. S., & Jolfaei, A. (2021). Energy-efficient drone trajectory planning for the localization of 6G-enabled IOT devices. IEEE Internet of Things Journal, 8(7), 5202–5210. https://doi.org/10.1109/jiot.2020.3032347

    Article  Google Scholar 

  24. Ebrahimi, D., Sharafeddine, S., Ho, P.-H., & Assi, C. (2021). Autonomous UAV trajectory for localizing ground objects: A reinforcement learning approach. IEEE Transactions on Mobile Computing, 20(4), 1312–1324. https://doi.org/10.1109/tmc.2020.2966989

    Article  Google Scholar 

  25. Farinha, A., Zufferey, R., Zheng, P., Armanini, S. F., & Kovac, M. (2020). Unmanned aerial sensor placement for cluttered environments. IEEE Robotics and Automation Letters, 5(4), 6623–6630. https://doi.org/10.1109/lra.2020.3015459

    Article  Google Scholar 

  26. Llombart, N., & Dabironezare, S. O. (2022). Feasibility study of quasi-optical MIMO antennas for radiative near-field links. IEEE Transactions on Antennas and Propagation, 70(8), 7073–7083. https://doi.org/10.1109/tap.2022.3168724

    Article  Google Scholar 

  27. Guidara, A., Fersi, G., Jemaa, M. B., & Derbel, F. (2021). A new deep learning-based distance and position estimation model for range-based indoor localization systems. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2021.102445

    Article  Google Scholar 

  28. Han, G., Jiang, J., Zhang, C., Duong, T. Q., Guizani, M., & Karagiannidis, G. K. (2016). A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Communications Surveys and Tutorials, 18(3), 2220–2243. https://doi.org/10.1109/comst.2016.2544751

    Article  Google Scholar 

  29. Zeng, Y., Xu, J., & Zhang, R. (2019). Energy minimization for wireless communication with rotary-wing UAV. IEEE Transactions on Wireless Communications, 18(4), 2329–2345. https://doi.org/10.1109/twc.2019.2902559

    Article  Google Scholar 

  30. Nadimi-Shahraki, M. H., Taghian, S., & Mirjalili, S. (2021). An improved grey wolf optimizer for solving engineering problems. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113917

    Article  Google Scholar 

  31. Saremi, S., Mirjalili, S. Z., & Mirjalili, S. M. (2015). Evolutionary population dynamics and grey wolf optimizer. Neural Computing & Applications, 26(5), 1257–1263. https://doi.org/10.1007/s00521-014-1806-7

    Article  Google Scholar 

  32. Mahi, M., Baykan, O. K., & Kodaz, H. (2015). A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Applied Soft Computing, 30, 484–490. https://doi.org/10.1016/j.asoc.2015.01.068

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hao Liu.

Ethics declarations

Conflict of interest

All authors declare that there is no conflict of interest.

Additional information

Publisher's Note

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

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

Liu, H., Chen, R., Ding, S. et al. Research on UAV-Aided WSNs Node Positioning Task Planning in Field Environment. Wireless Pers Commun 134, 1119–1152 (2024). https://doi.org/10.1007/s11277-024-10970-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-10970-2

Keywords

Navigation