Skip to main content
SpringerLink
Log in

A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Target tracking is an important application of sensor networks, particularly interesting for ecology applications related to wildlife monitoring. In this context, understanding the territorial occupation of animals is fundamental for understanding their habits. In this work, we propose the PRATIQUE—a prediction-based clustering algorithm for tracking targets considering a discrete sensor field divided into cells. This approach is based on two hierarchical levels: static clusters at the first level and dynamic clusters at the second level. This hybrid scheme reduces the cost of communication and ensures that all data generated by an event be delivered to a single node. We use Kalman, Alpha-Beta, or Particle Filters in order to predict the target’s position. Prediction is used to prepare the set of nodes that will detect the next event, thereby reducing the message overhead during the tracking task. Results show that prediction errors are close to one cell.

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

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Turgut, D., & Boloni, L. (2012). A pragmatic value-of-information approach for intruder tracking sensor networks. In 2012 IEEE International Conference on Communications (ICC) (pp. 4931–4936).

  3. Turgut, D., & Bölöni, L. (2013). Ive: Improving the value of information in energy-constrained intruder tracking sensor networks. In Proceedings of IEEE ICC’13 (pp. 4953–4957).

  4. Nakamura, E. F., Loureiro, A. A. F., & Orgambide, A. C. F. (2007). Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys, 39(3), 55 (Article 9).

  5. Souza, E. L., Campos, A., & Nakamura, E. F. (2011). Tracking targets in quantized areas with wireless sensor networks. In Proceedings of the 36th Local Computer Networks (LCN’11) (pp. 235–238). Germany: Bonn.

  6. Olfati-Saber, R. (2007). Distributed Kalman filtering for sensor networks. In Proceedings of the 46th Conference on Decision and Control (CDC’07) (pp. 5492–5498). New Orleans, USA.

  7. Sharma, S., Deshpande, S., & Sivalingam, K. M. (2011). Alpha-beta filter based target tracking in clustered wireless sensor networks. In Proceedings of the 3rd Conference on Communication Systems and Networks (COMSNETS’11) (pp. 1–4). Bangalore, India.

  8. Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

    Article  Google Scholar 

  9. Li, J., & Zhou, Y. (2010). Target tracking in wireless sensor networks. In Wireless Sensor Networks: Application-Centric Design (Chapter 19, pp. 1–20). InTech.

  10. Kung, H. T., & Vlah, D. (2003). Efficient location tracking using sensor networks. In Proceedings of the 57th Wireless Communications and Networking Conference (WCNC’03) (pp. 1954–1961). New Orleans, USA.

  11. Liu, B.-H. (2010). Effective reconstruction of the message-pruning trees in wireless sensor networks. In Proceedings of the 4th International Conference on Genetic and Evolutionary Computing (ICGEC’10) (pp. 695–698). Shenzhen, China.

  12. Bhatti, S., Xu, J., & Memon, M. (2011). Clustering and fault tolerance for target tracking using wireless sensor networks. IET Wireless Sensor Systems, 1(2), 66–73.

    Article  Google Scholar 

  13. Jian, Z., Chengdong, W., Peng, J., & Yue, H. (2011). Collaborative target tracking based on energy consideration in WSNs. In Proceedings of the 7th Conference on Wireless Communications, Networking and Mobile Computing (WiCOM’11) (pp. 1–4). Wuhan, China.

  14. Chang, W.-R., Lin, H.-T., & Cheng, Z.-Z. (2008). CODA: A continuous object detection and tracking algorithm for wireless ad hoc sensor networks. In Proceedings of the 5th Consumer Communications & Networking Conference (CCNC’08) (pp. 168–174). Las Vegas, Nevada, USA.

  15. Hajiaghajani, F., Naderan, M., Pedram, H., & Dehghan, M. (2012). HCMTT: Hybrid clustering for multi-target tracking in wireless sensor networks. In Proceedings of the 4th International Conference on Pervasive Computing and Communications Workshops (PERCOM’12) (pp. 889–894). Lugano, Switzerland.

  16. Deldar, F., & Yaghmaee, M. H. (2011). Designing an energy efficient prediction-based algorithm for target tracking in wireless sensor networks. In Proceedings of the Conference on Wireless Communications and Signal Processing (WCSP’11) (pp. 1–6). Nanjing, China.

  17. Suganya, S. (2008). A cluster-based approach for collaborative target tracking in wireless sensor networks. In Proceedings of the 1st Conference on Emerging Trends in Engineering and Technology (ICETET’08) (pp. 276–281). Nagpur, India.

  18. Lee, W., Yim, Y., Park, S., Lee, J., Park, H., & Kim, S.-H. (2011). A cluster-based continuous object tracking scheme in wireless sensor networks. In Proceedings of the Vehicular Technology Conference (VTC’11) (pp. 1–5). San Francisco, USA.

  19. Wang, C.-L., Chiou, Y.-S., & Dai, Y.-S. (2007). An adaptive location estimator based on Alpha-Beta filtering for wireless sensor networks. In Proceedings of the Wireless Communications and Networking Conference (WCNC’07) (pp. 3285–3290).

  20. Sharma, S., Deshpande, S., & Sivalingam, K. M. (2011). On guided navigation in target tracking sensor networks using alpha-beta filters. In Proceedings of the 31st Conference on Distributed Computing Systems Workshops (ICDCSW’11) (pp. 294–303). Minneapolis, Minnesota, USA.

  21. Djuric, P. M., Vemula, M., & Bugallo, M. F. (2008). Target tracking by particle filtering in binary sensor networks. IEEE Transactions on Signal Processing, 56(6), 2229–2238.

    Article  MathSciNet  Google Scholar 

  22. Ahmed, N., Rutten, M., Bessell, T., Kanhere, S. S., Gordon, N., & Jha, S. (2010). Detection and tracking using particle-filter-based wireless sensor networks. IEEE Transactions on Mobile Computing, 9(9), 1332–1345.

    Article  Google Scholar 

  23. Dong, H.-Y., Bin, C., & Yang, Y.-P. (2010). Application of particle filter for target tracking in wireless sensor networks. In Proceedings of the International Conference on Communications and Mobile Computing (CMC’10) (pp. 504–508). Shenzhen, China.

  24. Aydogmus, O., & Talu, M. F. (2012). Comparison of extended-Kalman- and particle-filter-based sensorless speed control. IEEE Transactions on Instrumentation and Measurement, 61(2), 402–410.

    Article  Google Scholar 

  25. Hou, S.-Y., Hung, H.-S., & Kao, T.-S. (2010). Extended Kalman particle filter angle tracking (EKPF-AT) algorithm for tracking multiple targets. In Proceedings of the International Conference on System Science and Engineering (ICSSE’10) (pp. 216–220). Taipei, Taiwan.

  26. Souza, É. L., Nakamura, E. F., de Oliveira, H. A. B. F., & Figueiredo, C. M. S. (2012). Reducing the impact of location errors for target tracking in wireless sensor networks. Journal of the Brazilian Computer Society, 19(1), 89–104.

    Article  Google Scholar 

  27. Campos, A. N., Souza, E. L., Nakamura, F. G., Nakamura, E. F., & Rodrigues, J. J. P. C. (2012). On the impact of localization and density control algorithms in target tracking applications for wireless sensor networks. Sensors, 12(6), 6930–6952.

    Article  Google Scholar 

  28. Figueiredo, C. M. S., Nakamura, E. F., Ribas, A. D., Souza, T. R. B., & Barreto, R. S. (2009). Assessing the communication performance of wsns in rainforests. In Proceedings of the 2nd IFIP Wireless Days (WD’09) (pp. 1–6). France, Paris.

  29. Guo, Y., Corke, P., Poulton, G., Wark, T., Bishop-Hurley, G., & Swain, D. (2006). Animal behaviour understanding using wireless sensor networks. In Proceedings of the 31st IEEE Conference on Local Computer Networks (LCN’06) (pp. 607–614). Tampa, Florida, USA.

  30. Kumar, A. A., & Sivalingam, K. M. (2012). Target tracking in a wsn with directional sensors using electronic beam steering. In Proceedings of the 4th Conference on Communication Systems and Networks (COMSNETS’12) (pp. 1–10). Bangalore, India.

  31. Xu, Y., Winter, J., & Lee, W.-C. (2004). Prediction-based strategies for energy saving in object tracking sensor networks. In Proceedings of the 5th International Conference on Mobile Data Management (MDM’04) (pp. 346–357). Berkeley, California, USA.

  32. Bhuiyan, M., Wang, G., 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.

    Article  Google Scholar 

  33. Hong, S.-W., Noh, S.-K., Lee, E., Park, S., & Kim, S.-H. (2010). Energy-efficient predictive tracking for continuous objects in wireless sensor networks. In Proceedings of the 21st International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC’10) (pp. 1725–1730). Istanbul, Turkey.

  34. Jian, X., & Li, J. (2012). Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering. Science China - Information Sciences, 3(3), 530–544.

    Google Scholar 

  35. Gustafsson, F. (2010). Particle filter theory and practice with positioning applications. IEEE Aerospace and Electronic Systems Magazine, 25(7), 53–82.

    Article  Google Scholar 

  36. Jiang, B., & Ravindran, B. (2011). Completely distributed particle filters for target tracking in sensor networks. In Proceedings of the 26th International Parallel Distributed Processing Symposium (IPDPS’11) (pp. 334–344). Shanghai, China.

  37. Zhao, Y., Yang, Y., & Kyas, M. (2011). Comparing centralized Kalman filter schemes for indoor positioning in wireless sensor network. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN’11) (pp. 1–10). Guimarães, Portugal.

  38. Julier, S. J., & Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.

    Article  Google Scholar 

  39. Gustafsson, F., & Hendeby, G. (2012). Some relations between extended and unscented Kalman filters. IEEE Transactions on Signal Processing, 60(2), 545–555.

    Article  MathSciNet  Google Scholar 

  40. LaViola, J. J. (2003). A comparison of Unscented and Extended Kalman filtering for estimating quaternion motion. In Proceedings of the 22th American Control Conference (ACC’03) (pp. 2435–2440). Denver, USA.

  41. Orderud, F. (2005). Comparison of Kalman filter estimation approaches for state space models with nonlinear measurements. In Proceedings of the Scandinavian Conference on Simulation and Modeling (SIMS’05). Trondheim, Norway.

  42. Jafaryahya, J., Najafi, S., Amindavar, H., & Dastmalchi, H. (2010). Sensor selection for target tracking in binary sensor networks using particle filter. In Proceedings of the 33rd IEEE Sarnoff Symposium (pp. 1–5). Princeton, New Jersey, USA.

  43. Machado, A. B. M., Martins, C. S., & Drummond, G. M. (2005). Lista da Fauna Brasileira Ameaçada de Extinção. Biodiversitas, 1 edition.

  44. Tsai, H.-W., Chu, C.-P., & Chen, T.-S. (2007). Mobile object tracking in wireless sensor networks. Computer Communications, 30(8), 1811–1825.

    Article  Google Scholar 

  45. Hsu, J.-M., Chen, C.-C., & Li, C.-C. (2012). POOT: An efficient object tracking strategy based on short-term optimistic predictions for face-structured sensor networks. Computers & Mathematics with Applications, 63(2), 391–406.

    Article  MATH  Google Scholar 

  46. Boukerche, A., Fernandes, H. A. B., de Oliveira, E., Nakamura, F., & Loureiro, A. A. F. (2007). Localization systems for wireless sensor networks. IEEE Wireless Communications, 14(6), 6–12.

    Article  Google Scholar 

  47. Aslam, J., Butler, Z., Constantin, F., Crespi, V., Cybenko, G., & Rus, D. (2003). Tracking a moving object with a binary sensor network. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03) (pp. 150–161). Los Angeles, USA.

  48. Dixiao, W., Dechao, Z., & Chuanbo, L. (2010). A new algorithm of target locating in binary wireless sensor networks. In Proceedings of the International Conference on Intelligent Computing and Intelligent Systems (ICIS’10) (pp. 753–756). Xiamen, China.

  49. Chen, W.-P., Hou, J. C., & Sha, L. (2004). Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 3(3), 258–271.

    Article  Google Scholar 

  50. Hsini, W., Li, B.-L., Springer, T. A., & Neill, W. H. (2000). Modelling animal movement as a persistent random walk in two dimensions: Expected magnitude of net displacement. Ecological Modelling, 132(2), 115–124.

    Google Scholar 

  51. Tan, G., & Kermarrec, A. M. (2011). Greedy geographic routing in large-scale sensor networks: A minimum network decomposition approach. IEEE/ACM Transactions on Networking, 20(3), 1–14.

    Google Scholar 

  52. Basagni, S., Carosi, A., Melachrinoudis, E., Petrioli, C., & Wang, Z. M. (2008). Controlled sink mobility for prolonging WSNs lifetime. Wireless Networks, 14(6), 831–858.

    Article  Google Scholar 

  53. Nakamura, E. F., & Souza, E. L. (2010). Towards a flexible event-detection model for wireless sensor networks. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC’10) (pp. 459–462). Riccione, Italy.

  54. Xu, Y., Winter, J., & Lee, W. C. (2004). Dual prediction-based reporting for object tracking sensor networks. In Proceedings of the 1st Conference on Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS’04) (pp. 154–163). Boston, Massachusetts, USA.

Download references

Author information

Authors and Affiliations

  1. Federal University of Amazonas – UFAM, Manaus, Brazil

    Éfren L. Souza & Eduardo F. Nakamura

  2. University of Ontario Institute of Technology – UOIT, Oshawa, Canada

    Éfren L. Souza & Richard W. Pazzi

  3. Federal University of Western Para – UFOPA, Santarém, Brazil

    Éfren L. Souza

  4. Analysis, Research and Technological Innovation Center Foundation – FUCAPI, Manaus, Brazil

    Eduardo F. Nakamura

Authors
  1. Éfren L. Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard W. Pazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduardo F. Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Éfren L. Souza.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Souza, É.L., Pazzi, R.W. & Nakamura, E.F. A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks. Wireless Netw 21, 2263–2278 (2015). https://doi.org/10.1007/s11276-015-0914-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-0914-3

Keywords

Search

Navigation