Skip to main content

Advertisement

SpringerLink
Log in

An adaptive target tracking method for 3D underwater wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UWSNs are considered in two-dimensional space. However, most practical applications require to be implemented in three-dimensional space. In this paper an adaptive method based on Kalman filter for moving target tracking in three dimensional space using UWSNs is proposed. Since, energy protection is a vital task in UWSNs; the proposed method reduces the energy consumption of the entire network by a sleep/wake plan. In this plan only 60% of the closer nodes along the path of the moving target will be waked up using a sink activation message and participate in the tracking, while the other nodes remain in sleep state. At each stage of tracking, the location of the target is estimated using a 3D underwater target tracking algorithm with the trilateration method. Subsequently, the estimations and target tracking results are inserted into the Kalman filter as measuring model to produce the final result. Performance evaluation and simulations results indicated that the proposed method improves the average location error by 45%, average estimated velocity by 86%, and average energy consumption by 33% in comparison to the trilateration method. However, computation time is increased as a result of improving tracking accuracy; and tracking accuracy is lost about 20% due to saving energy. It was shown that the proposed method has been able to adaptively achieve a trade-off between tracking accuracy and energy consumption based on real-time user requirements. Such adaption can be controlled trough the sink node based on real-time requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

References

  1. Arsanjani, T. J., Javidan, R., Nazemosadat, M. J., Arsanjani, J. J., & Vaz, E. (2015). Spatiotemporal monitoring of Bakhtegan Lake’s areal fluctuations and an exploration of its future status by applying a cellular automata model. Computers & Geosciences, 78, 37–43.

    Article  Google Scholar 

  2. Kavoosi, V., Dehghani, M. J., & Javidan, R. (2016). Selective geometry for near-field three-dimensional localization using one-pair sensor. IET Radar, Sonar and Navigation, 10(5), 844–849.

    Article  Google Scholar 

  3. Javidan, R., Masnadi-Shirazi, M. A., & Azimifar, Z. (2008). Contourlet-based acoustic seabed ground discrimination system. In 3rd IEEE International conference on information and communication technologies: from theory to applications.

  4. Javidan, R., & Jones, I. S. F. (2004). High resolution acoustic imaging of archaeological artifacts in fluid mud. In International congress on the application of recent advances in underwater detection and survey techniques to underwater archeology, Turkey.

  5. Mohammadi, R., Javidan, R., & Jalili, A. (2015). Fuzzy depth based routing protocol for underwater acoustic wireless sensor. Journal of Telecommunication, Electronic and Computer Engineering, 7(1), 81–86.

    Google Scholar 

  6. Kaiwartya, O., Abdullah, A. H., Cao, Y., Raw, R. S., Kumar, S., Lobiyal, D. K., et al. (2016). T-MQM: Testbed-based multi-metric quality measurement of sensor deployment for precision agriculture—A case study. IEEE Sensor Journal, 16(23), 8649–8664.

    Google Scholar 

  7. Khasawneh, A., Latiff, M. S. B. A., Kaiwartya, O., & Chizari, H. (2017). A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network. Wireless Networks. doi:10.1007/s11276-017-1461-x.

    Article  Google Scholar 

  8. Khasawneh, A., Latiff, M. S. B. A., Kaiwartya, O., & Kaiwartya, O. (2017). Next forwarding node selection in underwater wireless sensor networks (UWSNs): Techniques and challenges. MDPI- Information, 8(1), 1–30.

    Article  Google Scholar 

  9. Asif, M., Rizal, M., & Yahya, A. (2006). An active contour for underwater target tracking and navigation. In Proceedings of International Conference on Man-Machine Systems, Langkawi Islands, Malaysia, pp. 1–6.

  10. Dalberg, E., Lauberts, A., Lennartsson, R. K., Levonen, M. J., & Persson, L. (2006). Underwater target tracking by means of acoustic and electromagnetic data fusion. In Proceedings of 9th International Conference on Information Fusion, Florence, Italy, pp. 1–7.

  11. Pettersson, M. I., Zetterberg, V., & Claesson, I. (2005). Detection and imaging of moving targets in wideband SAS using fast time backprojection combined with space–time processing. In Proceedings of MTS/IEEE Oceans, Vol. 3, pp. 2388–2393.

  12. Eickstedt, D., Benjamin, M., Schmidt, H., & Leonard, J. (2006). “Adaptive tracking of underwater targets with autonomous sensor networks. Journal of Underwater Acoustics, 56, 465–495.

    Google Scholar 

  13. Wang, G., Alam Bhuiyan, Z., Cao, J., & Wu, J. (2014). Detecting movements of a target using face tracking in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(4), 939–949.

    Article  Google Scholar 

  14. Zheng, J., Bhuiyan, M., Liang, S., Xing, X., & Wang, G. (2014). Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks. Ubiquitous Computing and Future Communication Systems, 39, 88–99.

    Google Scholar 

  15. Pino-Povedano, S., Arroyo-Valles, R., & Cid-Sueiro, J. (2014). Selective forwarding for energy-efficient target tracking in sensor networks. Signal Processing, 94, 557–569.

    Article  Google Scholar 

  16. An, Y., Yoo, S., An, C., & Wells, B. (2013). Doppler effect on target tracking in wireless sensor networks. Computer Communications, 36, 834–848.

    Article  Google Scholar 

  17. Yu, C., Lee, K., Choi, J., & Seo, Y. (2008). Distributed single target tracking in underwater wireless sensor networks. In Proceedings of SICE Annual Conference, Tokyo, Japan, pp. 1351–1356.

  18. Uludag, S., Karakus, M., & Guler, E. (2014). Low-complexity 3D target tracking in wireless aerial sensor networks. In Proceedings of IEEE International Conference Communications, Sydney, pp. 373–378.

  19. Isbitiren, G., & Akan, O. B. (2011). Three-dimensional underwater target tracking with acoustic sensor networks. IEEE Transactions on Vehicular Technology, 60(8), 3897–3906.

    Article  Google Scholar 

  20. Hare, J., Gupta, S., & Song, J. (2014). Distributed smart sensor scheduling for underwater target tracking. In Proceedings of Oceans Conference, St. John’s, NL.

  21. Zhang, Q., Liu, M., & Zhang, S. (2015). Node topology effect on target tracking based on UWSNs using quantized measurements. IEEE Transactions on Cybernetics, 45(10), 2323–2335.

    Article  Google Scholar 

  22. Ramezani, H., & Jamali, H. (2013). Target localization and tracking for an isogradient sound speed profile. IEEE Transactions on Signal Processing, 61(6), 1434–1446.

    Article  MathSciNet  Google Scholar 

  23. Li, W., Li, Y., Ren, S., & Feng, X. (2013). Tracking an underwater maneuvering target using an adaptive Kalman filter. In Proceedings of IEEE Conference TENCON, Xi’an.

  24. Li, S., Li, X., Wang, X., & Liuy, J. (2016). Sequential hypothesis test with online usage-constrained sensor selection. https://arxiv.org/abs/1601.06447.

  25. Li, S., & Wang, X. (2016). Optimal joint detection and estimation based on decision-dependent Bayesian cost. IEEE Transactions on Signal Processing, 64(10), 2573–2586.

    Article  MathSciNet  Google Scholar 

  26. Liu, X.-Y., Zhu, Y., Kong, L., Cong Liu, Y., Vasilakos, A. V., & Min-You, W. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.

    Article  Google Scholar 

  27. Liu, X.-Y., Aeron, S., Aggarwal, V., Wang, X., & Min-You, W. (2016). Adaptive sampling of RF fingerprints for fine-grained indoor localization. IEEE Transactions on Mobile Computing, 15(10), 2411–2423.

    Article  Google Scholar 

  28. Waite, A. D. (2001). SONAR for practicing engineers (3rd ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  29. Stojanovic, M. (2006). On the relationship between capacity and distance in an underwater acoustic communication channel. In Proceedings of the 1st ACM international workshop on underwater networks, Los Angeles, California, USA, pp. 41–47.

  30. Isik, M. T., & Akan, O. B. (2009). A three-dimensional localization algorithm for underwater acoustic sensor networks. IEEE Transactions on Wireless Communications, 8(9), 4457–4463.

    Article  Google Scholar 

  31. Zhou, Z., Cui, J. H., & Zhou, S. (2007). Localization for large-scale underwater sensor networks. In Proceedings of IFIP Networking, pp. 108–119.

    Chapter  Google Scholar 

  32. Moore, D., Leonard, J., Rus, D., & Teller, S. (2004). Robust distributed network localization with noisy range measurements. In Proceedings of ACM Sensor Systems, Baltimore, MD.

  33. Xiao, W., Xie, L., Lin, J., & Li, J. (2006). Multi-sensor scheduling for reliable target tracking in wireless sensor networks. In Proceedings of 6th International Conference on ITS Telecommunications, Chengdu, pp. 996–1000.

  34. Li, C., Chang, Y., Hung, C., & Chuang, C. (2015). Position estimation and smooth tracking with a fuzzy-logic-based adaptive strong tracking Kalman filter for capacitive touch panels. IEEE Transactions on Industrial Electronics, 62(8), 5097–5108.

    Article  Google Scholar 

  35. Hu, X., Hu, Y., & Xu, B. (2014). Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network. IET Signal Processing, 8(5), 467–474.

    Article  Google Scholar 

  36. Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks, 3(3), 257–279.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

    Mehrnaz Poostpasand & Reza Javidan

Authors
  1. Mehrnaz Poostpasand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Javidan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Javidan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poostpasand, M., Javidan, R. An adaptive target tracking method for 3D underwater wireless sensor networks. Wireless Netw 24, 2797–2810 (2018). https://doi.org/10.1007/s11276-017-1506-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1506-1

Keywords

Search

Navigation