Face-Based Mobile Target Tracking Technique in Wireless Sensor Network

  • Khalid AliEmail author
  • Mohd Fadlee A. Rasid
  • Aduwati Sali
  • Borhanuddin Ali


A prediction-based method is presented to track mobile object and its location in a sensor network area. In recent years, energy consumption and high accuracy target tracking have been a challenge in wireless sensor network. A number of applications have been used to reduce energy depletion by involving only a few number of sensor nodes to contribute in communication, sensing for target tracking, and transaction. In this study, the Face-based Target Tracking Technique (FTTT) is used to minimise energy depletion to extend the lifetime of sensor node as well as to track object accurately. FTTT combines prediction algorithm with face routing, which can produce accurate detection. At the beginning, sensor node in the border detects the object and elects Triangular sensor Nodes (TN) in the face structure which are the nearest to the object. The process is then continued with TNs tracking the Moving Object (MO) and predicting its next position by face routing structure. The next set of face-based structures will activate TN and offer continuous tracking of the MO. The simulation of the proposed technique are evaluated against the existing approach in terms of object tracking, object detection, speed monitoring as well as energy consumption. The results of FTTT show high accuracy of object detection with less energy consumption.


Face-based Target tracking Prediction Accuracy Energy efficiency Wireless sensor networks 



  1. 1.
    Hai-bo, Y., Ning, Q., & You-rong, C. (2009). An object tracking technique in wireless sensor network based on prediction. In International conference on communication software and networks, 2009. ICCSN’09 (pp. 3–8). IEEE.Google Scholar
  2. 2.
    Alaybeyoglu, A., Erciyes, K., Kantarci, A., & Dagdeviren, O. (2010). Tracking fast moving targets in wireless sensor networks. IETE Technical Review, 27(1), 46–53.CrossRefGoogle Scholar
  3. 3.
    Alaybeyoglu, A., Kantarci, A., & Erciyes, K. (2010). A dynamic distributed tree based tracking algorithm for wireless sensor networks. In Recent trends in wireless and mobile networks (pp. 295–303). Berlin: Springer.Google Scholar
  4. 4.
    Lin, C. Y., & Tseng, Y. C. (2004). Structures for in-network moving object tracking in wireless sensor networks. In First international conference on broadband networks, 2004. BroadNets 2004. Proceedings (pp. 718–727). IEEE.Google Scholar
  5. 5.
    Lin, C. Y., Peng, W. C., & Tseng, Y. C. (2006). Efficient in-network moving object tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 5(8), 1044–1056.CrossRefGoogle Scholar
  6. 6.
    Yeong-Sung, F., & Hsu, Y. Y. (2010, June). An energy-efficient algorithm for object tracking in Wireless Sensor Networks. In 2010 IEEE international conference on wireless communications, networking and information security (pp. 424–430). IEEE.Google Scholar
  7. 7.
    Liu, B. H., Ke, W. C., Tsai, C. H., & Tsai, M. J. (2008). Constructing a message-pruning tree with minimum cost for tracking moving objects in wireless sensor networks is NP-complete and an enhanced data aggregation structure. IEEE Transactions on Computers, 57(6), 849–863.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hsu, J. M., Chen, C. C., & Li, C. C. (2011, June). Short-term prediction-based optimistic object tracking strategy in wireless sensor networks. In 2011 fifth international conference on innovative mobile and internet services in ubiquitous computing (IMIS) (pp. 78–85). IEEE.Google Scholar
  9. 9.
    Wang, G., Bhuiyan, M. Z. A., 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.CrossRefGoogle Scholar
  10. 10.
    Bhuiyan, M. Z. A., Wang, G., & Wu, J. (2009). Target tracking with monitor and backup sensors in wireless sensor networks. In 2009 Proceedings of 18th international conference on computer communications and networks (pp. 1–6). IEEE.Google Scholar
  11. 11.
    Bhuiyan, M. Z. A., Wang, G. J., Zhang, L., & Peng, Y. (2010). Prediction-based energy-efficient target tracking protocol in wireless sensor networks. Journal of Central South University of Technology, 17(2), 340–348.CrossRefGoogle Scholar
  12. 12.
    Chen, T. S., Liao, W. H., Huang, M. D., & Tsai, H. W. (2005, November). Dynamic object tracking in wireless sensor networks. In 2005 13th IEEE international conference on networks, 2005 jointly held with the 2005 IEEE 7th Malaysia international conference on communication (Vol. 1). IEEE.Google Scholar
  13. 13.
    Kulathumani, V., Arora, A., Demirbas, M., & Sridharan, M. (2007, January). Trail: A distance sensitive WSN service for distributed object tracking. In European conference on wireless sensor networks (pp. 83–100). Berlin: Springer.Google Scholar
  14. 14.
    Bose, P., Morin, P., Stojmenovi, I., & Urrutia, J. (2001). Routing with guaranteed delivery in ad hoc wireless networks. Wireless Networks, 7(6), 609–616.CrossRefGoogle Scholar
  15. 15.
    Tsai, H. W., Chu, C. P., & Chen, T. S. (2007). Mobile object tracking in wireless sensor networks. Computer Communications, 30(8), 1811–1825.CrossRefGoogle Scholar
  16. 16.
    Xu, Y., Winter, J., & Lee, W. C. (2004). Dual prediction-based reporting for object tracking sensor networks. In The first annual international conference on mobile and ubiquitous systems: networking and services, 2004. MOBIQUITOUS 2004 (pp. 154–163). IEEE.Google Scholar
  17. 17.
    Mohanoor, A. B., Radhakrishnan, S., & Sarangan, V. (2009). Online energy aware routing in wireless networks. Ad Hoc Networks, 7(5), 918–931.CrossRefGoogle Scholar
  18. 18.
    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking (ToN), 11(1), 2–16.CrossRefGoogle Scholar
  19. 19.
    Naderan, M., Dehghan, M., Pedram, H., & Hakami, V. (2012). Survey of mobile object tracking protocols in wireless sensor networks: A networkcentric perspective. International Journal of Ad Hoc and Ubiquitous Computing, 11(1), 34–63.CrossRefGoogle Scholar
  20. 20.
    Huang, Q., Lu, C., & Roman, G. C. (2004). Reliable mobicast via face-aware routing. In INFOCOM 2004. twenty-third annual joint conference of the IEEE computer and communications societies (Vol. 3, pp. 2108–2118). IEEE.Google Scholar
  21. 21.
    Karp, B., & Kung, H. T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on mobile computing and networking (pp. 243–254). ACM.Google Scholar
  22. 22.
    Chui, C. K., & Chen, G. (2009). Kalman filtering with real-time applications. Springer series in Information Sciences (4th ed.). New York, NY: Springer.Google Scholar
  23. 23.
    Haseltine, E. L., & Rawlings, J. B. (2005). Critical evaluation of extended Kalman filtering and moving-horizon estimation. Industrial & Engineering Chemistry Research, 44(8), 2451–2460.CrossRefGoogle Scholar
  24. 24.
    Tsukamoto, K., Ueda, H., Tamura, H., Kawahara, K., & Oie, Y. (2009). Design of wireless sensor network for multi-point surveillance of a moving target based on the relationship between tracking probability and sensor density. In Consumer communications and networking conference, 2009. CCNC 2009. 6th IEEE (pp. 1–5). IEEE.Google Scholar
  25. 25.
    Mahgoub, I., & Ilyas, M. (2016). Sensor network protocols. Boca Raton: CRC Press.Google Scholar

Copyright information

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

Authors and Affiliations

  • Khalid Ali
    • 1
    Email author
  • Mohd Fadlee A. Rasid
    • 1
  • Aduwati Sali
    • 1
  • Borhanuddin Ali
    • 1
  1. 1.Universiti Putra Malaysia Fakulti KejuruteraanSerdangMalaysia

Personalised recommendations