Skip to main content

Advertisement

Log in

Reliable Target Tracking Model Employing Wireless Sensor Networks

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The wireless sensor networks (WSN) provides advancement of number of revolutionary applications such as localization, target tracking, etc. Most of these applications involve a numerous sensor device that are connected to the base station which behaves as a gateway to connect internally and cloud computing environments. The key operation of WSNs is data collection, data sensing and transmission. However, the sensor devices gather data and is communicated over the intermediate node in an episodic manner for smart decisions periodically. Enhancing the tracking prediction accuracy, reliability of network and lifetime performance for data gathered is the important objective of target tracking applications using WSNs. This work presents Reliable Target Tracking (RTT) model employing WSNs. First, in achieving higher prediction accuracy a Modified Kalman Filter (MKF) is introduced. Second, improved cluster head (CH) selection and multi-objective-based route optimization are presented. Experiment results shows the RTT model achieves major outcome when compared with present target tracking model employing WSNs for improving energy efficiency, tracking accuracy, latency reduction and communication overhead.

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

Similar content being viewed by others

Data availability

There is no data obtained for this report as complete details available in literature survey.

References

  1. Zou X, Li L, Du H, Zhou L. Intelligent sensing and computing in wireless sensor networks for multiple target tracking. Journal of Sensors. 2022. https://doi.org/10.1155/2022/2870314. (2870314).

    Article  Google Scholar 

  2. Feng J, Zhao H. Dynamic nodes collaboration for target tracking in wireless sensor networks. IEEE Sens J. 2021;21(18):21069–79. https://doi.org/10.1109/JSEN.2021.3093473.

    Article  Google Scholar 

  3. Pang C, Xu G-g, Shan G-l, Zhang Y-p. A new energy efficient management approach for wireless sensor networks in target tracking. Defence Technol. 2021;17(3):932–47. https://doi.org/10.1016/j.dt.2020.05.022.

    Article  Google Scholar 

  4. Nayak P, Devulapalli A. A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens J. 2016;16(1):137–44.

    Article  Google Scholar 

  5. Nayak P, Vathasavai B. Energy efficient clustering algorithm for multi-hop wireless sensor network using type-2 fuzzy logic. IEEE Sens J. 2017;17(14):4492–9.

    Article  Google Scholar 

  6. Ang KLM, Seng JKP, Zungeru AM. Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. IEEE Syst J. 2017;99:1–11.

    Google Scholar 

  7. Rani S, Ahmed SH, Talwar R, Malhotra J. Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Trans Industr Inf. 2017;13(4):1961–8.

    Article  Google Scholar 

  8. Liu X, Li J, Dong Z, Xiong F. Joint design of energy-efficient clustering and data recovery for wireless sensor networks. IEEE Access. 2017;5:3646–56.

    Article  Google Scholar 

  9. Twayej W, Khan M, Al-Raweshidy HS. Network performance evaluation of M2M with self-organizing cluster head to sink mapping. IEEE Sens J. 2017;17(15):4962–74.

    Article  Google Scholar 

  10. Deva Sarma HK, Mall R, Kar A. E2R2: energy-efficient and reliable routing for mobile wireless sensor networks. IEEE Syst J. 2016;10(2):604–16.

    Article  Google Scholar 

  11. Gianluigi F, Mengjia Z, Xu H, Bo Z, Xiangxiang F. A heterogeneous energy wireless sensor network clustering protocol. Wirel Commun Mob Comput. 2019. https://doi.org/10.1155/2019/7367281.

    Article  Google Scholar 

  12. Qiu T, Zhang Y, Qiao D, Zhang X, Wymore ML, Sangaiah AK. A robust time synchronization scheme for industrial Wireless sensor networks. IEEE Trans Ind Informat. 2018;14(8):3570–80.

    Article  Google Scholar 

  13. Liu Y, et al. QTSAC: an energy-efficient MAC protocol for delay minimization in wireless sensor networks. IEEE Access. 2018;6:8273–91.

    Article  Google Scholar 

  14. Jurado-Lasso FF, Clarke K, Nirmalathas A. A software-defined management system for IP-enabled WSNs. IEEE Syst J. 2020;14(2):2335–46. https://doi.org/10.1109/JSYST.2019.2946781.

    Article  Google Scholar 

  15. Xiang X, Liu W, Wang T, Xie M, Li X, Song H, Liu A, Zhang G. Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic WSN. EURASIP J Wirel Commun Netw. 2019.

  16. Kulkarni PKH, MalathiJesudason P. Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation. IET Commun. 2019;13(11):1685–95.

    Article  Google Scholar 

  17. Kumar P, Kulkarni H, Malathi P. PFuzzyACO: fuzzy-based optimization approach for energy-aware cluster head selection in WSN. J Internet Technol. 2019;20(6):1787–800.

    Google Scholar 

  18. Chauhan V, Soni S. Mobile sink-based energy efficient cluster head selection strategy for wireless sensor networks. J Ambient Intell Human Comput. 2020;11:4453–66. https://doi.org/10.1007/s12652-019-01509-6.

    Article  Google Scholar 

  19. Sangaiah AK, et al. Energy-aware geographic routing for real-time workforce monitoring in industrial informatics. IEEE Internet of Things J. 2021;8(12):9753–62. https://doi.org/10.1109/JIOT.2021.3056419.

    Article  Google Scholar 

  20. Pang C, Xu G, Shan G, Zhang Y. A new energy efficient management approach for wireless sensor networks in target tracking. Defence Technol. 2020. https://doi.org/10.1016/j.dt.2020.05.022.

    Article  Google Scholar 

  21. Zhang H, Zhou X, Wang Z, Yan H. Maneuvering target tracking with event-based mixture Kalman filter in mobile sensor networks. IEEE Trans Cybern. 2020;50(10):4346–57. https://doi.org/10.1109/TCYB.2019.2901515.

    Article  Google Scholar 

  22. Liu F, Jiang C, Xiao W. Multistep prediction-based adaptive dynamic programming sensor scheduling approach for collaborative target tracking in energy harvesting wireless sensor networks. IEEE Trans Autom Sci Eng. 2021;18(2):693–704. https://doi.org/10.1109/TASE.2020.3019567.

    Article  Google Scholar 

  23. Shnitzer T, Talmon R, Slotine J-J. Diffusion maps Kalman filter for a class of systems with gradient flows. IEEE Trans Signal Process. 2020. https://doi.org/10.1109/TSP.2020.2987750.

    Article  MathSciNet  MATH  Google Scholar 

  24. Kumar S, Sudhir, Tiwari UK. Energy efficient target tracking with collision avoidance in WSNs. Wirel Pers Commun. 2018;103:2515–28. https://doi.org/10.1007/s11277-018-5944-6.

    Article  Google Scholar 

  25. Lokesh D, Reddy NV. Energy efficient target tracking method for multi-sensory scheduling in wireless sensor networks. 2020.

  26. Al-Karaki JN, Al-Mashaqbeh GA. SENSORIA: a new simulation platform for wireless sensor networks. In: 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), Valencia, 2007; pp. 424–9.

  27. Rayudu, DM, Naresh E, Vijaya Kumar BP. The impact of test-driven development on software defects and cost: a comparative case study. Int J Comput Eng Technol (IJCET). 2014;5(2).

  28. Naresh E, Kalaskar SK. A Novel Testing Methodology to Improve the quality of testing a GUI application. MSR J Eng Technol Res. 2013;1(1):41–6.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. Naresh.

Ethics declarations

Conflict of Interest

The authors do not have any conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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

Chaitra, H.V., Patil, M., Manjula, G. et al. Reliable Target Tracking Model Employing Wireless Sensor Networks. SN COMPUT. SCI. 4, 446 (2023). https://doi.org/10.1007/s42979-023-01872-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01872-4

Keywords

Navigation