Peer-to-Peer Networking and Applications

, Volume 12, Issue 5, pp 1041–1060 | Cite as

A type of energy-efficient target tracking approach based on grids in sensor networks

  • Chao ShaEmail author
  • Lian-hua Zhong
  • Yao Bian
  • Dan-dan Song
  • Chun-hui Ren


To enhance the reliability as well as the value of sensing data in Wireless Sensor Networks (WSNs), a type of Energy-efficient Target Tracking Approach (ETTA) is proposed in this paper. The sensor network is divided into several virtual grids for distributed tracking and three kinds of states (tracking state, prepared-tracking state and preparing-tracking state) of these grids are also proposed to reduce energy consumption and enhance the accuracy of node localization. Moreover, a tracking recovery strategy is also described in this paper that effectively enhance the robustness of the tracking system. Experiment results show that ETTA has a good performance on target tracking in sensor networks compared to BPS and EMTT.


Sensor networks Target tracking Virtual grids Energy-efficient Network lifetime 



The subject is sponsored by the National Natural Science Foundation of P.R. China (61872194), Jiangsu Natural Science Foundation for Excellent Young Scholar (BK20160089), Six Talent Peaks Project of Jiangsu Province (JNHB-095), “333” Project of Jiangsu Province, Qing Lan Project of Jiangsu Province, Innovation Project for Postgraduate of Jiangsu Province (KYCX17_0796, KYCX17_0797, SJCX17_0238, SJCX18_0295) and 1311 Talents Project of Nanjing University of Posts and Telecommunications.

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  1. 1.
    Jiang W, Chen SR, Cai BG et al (2018) A multi-sensor positioning method-based train localization system for low density line[J]. IEEE Trans Veh Technol 67(11):10425–10437Google Scholar
  2. 2.
    Kim S, Kim DY (2018) Efficient data-forwarding method in delay-tolerant P2P networking for IoT services[J]. Peer-to-Peer Networking and Applications 11(6):1176–1185Google Scholar
  3. 3.
    Wang T, Peng Z, Liang J et al (2016) Following targets for Mobile tracking in wireless sensor networks[J]. ACM Transactions on Sensor Networks 12(4):1–24Google Scholar
  4. 4.
    Cui J, Shao LL, Zhong H et al (2018) Data aggregation with end-to-end confidentiality and integrity for large-scale wireless sensor networks[J]. Peer-to-Peer Networking and Applications 11(5):1022–1037Google Scholar
  5. 5.
    Wu W, Xiong N, Wu C (2017) Improved clustering algorithm based on energy consumption in wireless sensor networks[J]. IET Networks 6(3):47–53Google Scholar
  6. 6.
    Chen T, Chen JJ, Wu CH (2016) Distributed object tracking using moving trajectories in wireless sensor networks[J]. Wirel Netw 22(7):2415–2437Google Scholar
  7. 7.
    Sha C, Wang QW, Zhang L, Wang RC (2018) A high-efficiency data collection method based on maximum recharging benefit in sensor networks[J]. Sensors 18(9):2887–2920Google Scholar
  8. 8.
    Sha C, Liu Q, Song SY, Wang RC (2018) A type of annulus-based energy balanced data collection method in wireless rechargeable sensor networks[J]. Sensors 18(9):3150–3178Google Scholar
  9. 9.
    Satish RJ, Rajkumar SD (2019) Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks[J]. IEEE Sensors J 19(1):224–233Google Scholar
  10. 10.
    Engin M, Abdulkadir K (2018) A proportional time allocation algorithm to transmit binary sensor decisions for target tracking in a wireless sensor network[J]. IEEE Trans Signal Process 66(1):86–100MathSciNetzbMATHGoogle Scholar
  11. 11.
    Banaezadeh F, Haghighat AT (2015) Evaluation ARIMA modeling-based target tracking scheme in wireless sensor networks using statistical tests[J]. Wirel Pers Commun 84(4):1–13Google Scholar
  12. 12.
    Guo YN, Cheng J, Liu HY, Gong D, Xue Y (2017) A novel knowledge-guided evolutionary scheduling strategy for energy-efficient connected coverage optimization in WSNs[J]. Peer-to-Peer Networking and Applications 10(3):547–558Google Scholar
  13. 13.
    Souza FL, Pazzi RW, Nakamura EF (2015) A prediction- based clustering algorithm for tracking targets in quantized areas for wireless sensor networks[J]. Wirel Netw 21(7):2263–2278Google Scholar
  14. 14.
    Kung, H. T.; Vlah, D. Efficient location tracking using sensor networks[C]. In Proceedings of the 57th Wireless Communications and Networking Conference, New Orleans, USA, 16–20 March, 2003, 1954–1961Google Scholar
  15. 15.
    Liu BH Effective reconstruction of the message pruning trees in wireless sensor networks[C]. In: Proceedings of the 4th international conference on genetic and evolutionary computing, Shenzhen, China, 13–15 December 2010, pp 695–698Google Scholar
  16. 16.
    Zhang W, Cao G (2004) DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks[J]. IEEE Transactions on Wireless Communication 3(5):1689–1701Google Scholar
  17. 17.
    Mehta K, Liu D, Wright M (2012) Protecting location privacy in sensor networks against a global eavesdropper[J]. IEEE Trans Mob Comput 11(2):320–336Google Scholar
  18. 18.
    Alaybeyoglu A, Kantarci A, Erciyes K (2014) A dynamic look ahead tree based tracking algorithm for wireless sensor networks using particle filtering technique[J]. Computers & Electrical Engineering 40(2):374–383Google Scholar
  19. 19.
    Alberto de SB, Jose RMD, Anibal O (2015) Efficient cluster-based tracking mechanisms for camera-based wireless sensor networks[J]. IEEE Trans Mob Comput 14(9):1820–1832Google Scholar
  20. 20.
    Bhatti S, Xu J, Memon M (2011) Clustering and fault tolerance for target tracking using wireless sensor networks[J]. IET Wireless Sensor Systems 1(2):66–73Google Scholar
  21. 21.
    Teng J, Snoussi H, Richard C et al (2012) Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks[J]. IEEE Trans Veh Technol 61(5):2305–2318Google Scholar
  22. 22.
    Enayet A, Razzaque MA, Hassan MM et al (2014) Moving target tracking through distributed clustering in directional sensor networks[J]. Sensors 14(12):24381–24407Google Scholar
  23. 23.
    Fu P, Cheng Y, Tang H, Li B, Pei J, Yuan X (2017) An effective and robust decentralized target tracking scheme in wireless camera sensor networks[J]. Sensors 17(3):639–662Google Scholar
  24. 24.
    Jiang B, Ravindran B, Cho H (2013) Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks[J]. IEEE Trans Mob Comput 12(4):735–747Google Scholar
  25. 25.
    Xu Y, Winter J, Lee W-C (2004) Prediction-based strategies for energy saving in object tracking sensor networks[C]. In: IEEE international conference on Mobile data management, Berkeley, CA, USA, 19–22 Jan, pp 346–357Google Scholar
  26. 26.
    Turgut D, Bölöni LIVE Improving the value of information in energy-constrained intruder tracking sensor networks[C]. In: 2013 IEEE international conference on communications, Budapest, Hungary, 9–13 June 2013, pp 6360–6364Google Scholar
  27. 27.
    Taqi RM, Hameed MZ, Hammad AA et al (2008) Adaptive yaw rate aware sensor wakeup schemes protocol (A-YAP) for target prediction and tracking in sensor networks[J]. IEICE Trans Commun 9(11):3524–3533Google Scholar
  28. 28.
    Hsua JM, Chenb CC, Li CC (2012) An efficient object tracking strategy based on short-term optimistic predictions for face-structured sensor networks[J]. Computers & Mathematics with Applications 63(2):391–406Google Scholar
  29. 29.
    Olfati-Saber, R. Distributed Kalman filtering for sensor networks[C]. 46th IEEE Conference on Decision and Control, New Orleans, LA, 12–14 December, 2007, 5492–5498Google Scholar
  30. 30.
    Wang F, Bai X, Guo B (2016) Dynamic clustering in wireless sensor network for target tracking based on the fisher information of modified Kalman filter[C]. In: International conference on systems and informatics, Shanghai, China, 19–21 November, pp 696–700Google Scholar
  31. 31.
    He J, Xiong N, Xiao Y et al (2010) A reliable energy efficient algorithm for target coverage in wireless sensor networks[C]. In: IEEE 30th international conference on distributed computing systems workshops, Genova, Italy, 21–25 June, pp 180–188Google Scholar
  32. 32.
    Chen YR, Lu SY, Chen JJ et al (2017) Node localization algorithm of wireless sensor networks with mobile beacon node[J]. Peer-to-Peer Networking and Applications 10(3):795–807Google Scholar
  33. 33.
    Yuan YL, Huo LW, Wang ZX et al (2018) Secure APIT localization scheme against Sybil attacks in distributed wireless sensor networks[J]. IEEE Access 6:27629–27636Google Scholar
  34. 34.
    Kim W, Mechitov K, Choi JY, Ham S (April 2005) On target tracking with binary proximity sensors[C]. Fourth international symposium on information procession in sensor networks, Boise, Idaho. USA 15-16:301–308Google Scholar
  35. 35.
    Wen Y, Gao R, Zhao H (2016) Energy efficient moving target tracking in wireless sensor networks[J]. Sensors 16(1):1–11Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chao Sha
    • 1
    • 2
    Email author
  • Lian-hua Zhong
    • 1
    • 2
  • Yao Bian
    • 3
  • Dan-dan Song
    • 1
    • 2
  • Chun-hui Ren
    • 1
    • 2
  1. 1.School of Computer Science, Software and Cyberspace SecurityNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworksNanjingChina
  3. 3.School of Oversea EducationNanjing University of Posts and TelecommunicationsNanjingChina

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