On Topology of Sensor Networks Deployed for Tracking

  • Ye Zhu
  • Anil Vikram
  • Huirong Fu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6843)


In this paper, we study topologies of sensor networks deployed for tracking multiple targets with Blind Source Separation (BSS), a statistical signal processing technique widely used to recover individual signals from mixtures of signals. BSS-based tracking algorithms are proven to be effective in tracking multiple indistinguishable targets. The topology of a wireless sensor network deployed for tracking with BSS-based algorithms is critical to tracking performance: (a) The topology affects separation performance. (b) The topology determines accuracy and precision of estimation on the paths taken by targets. We propose cluster topologies for BSS-based tracking algorithms. Guidelines on parameter selection for proposed topologies are given in this paper. We evaluate proposed cluster topologies with extensive experiments. Our empirical experiments also show that BSS-based tracking algorithm can achieve comparable tracking performance in comparison with algorithms assuming single target or distinguishable targets.


Sensor Network Tracking Performance Blind Source Separation Error Distance Separation Performance 
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  1. 1.
    Bai, X., Kumar, S., Xuan, D., Yun, Z., Lai, T.H.: Deploying wireless sensors to achieve both coverage and connectivity. In: MobiHoc 2006: Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 131–142. ACM, New York (2006)Google Scholar
  2. 2.
    Cardoso, J.: Blind signal separation: statistical principles 9(10), 2009–2025 (1998), Google Scholar
  3. 3.
    Chan, H., Luk, M., Perrig, A.: Using clustering information for sensor network localization. In: Prasanna, V.K., Iyengar, S.S., Spirakis, P.G., Welsh, M. (eds.) DCOSS 2005. LNCS, vol. 3560, pp. 109–125. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Comon, P.: Independent component analysis, a new concept? Signal Process 36(3), 287–314 (1994)CrossRefzbMATHGoogle Scholar
  5. 5.
    Friedlander, D.S., Phoha, S.: Semantic information fusion for coordinated signal processing in mobile sensor networks. The International Journal of High Performance Computing Applications (2002)Google Scholar
  6. 6.
    Gaeta, M., Lacoume, J.L.: Source separation without a priori knowledge: the maximum likelihood solution. In: Proc. EUSIPCO, pp. 621–624 (1990)Google Scholar
  7. 7.
    Hardy, J.W.: Sounds of Florida’s Birds (1998),
  8. 8.
    He, T., Vicaire, P., Yan, T., Luo, L., Gu, L., Zhou, G., Stoleru, R., Cao, Q., Stankovic, J.A., Abdelzaher, T.: Achieving real-time target tracking usingwireless sensor networks. In: RTAS 2006: Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 37–48. IEEE Computer Society, Washington, DC (2006)Google Scholar
  9. 9.
    Hyvrinen, A.: Fast and robust xed-point algorithms for independent component analysis. IEEE Trans. on Neural Networks 10 (1999)Google Scholar
  10. 10.
    Kim, W., Mechitov, K., Choi, J.Y., Ham, S.: On target tracking with binary proximity sensors. In: IPSN 2005: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, p. 40. IEEE Press, Piscataway (2005)Google Scholar
  11. 11.
    Kinsler, et al.: Fundamentals of Acoustics. John Wiley, New York (2000)zbMATHGoogle Scholar
  12. 12.
    Kershner, R.: The number of circles covering a set. American Journal of Mathematics 61, 665–671 (1939)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Scholl, J.F., Agre, J.R., Clare, L.P.: Wavelet packet target classification schemes. In: Proc. 1999 Meeting of the MSS Specialty Group on Battlefield Acoustic and Seismic Sensing (1999)Google Scholar
  14. 14.
    Shrivastava, N., Mudumbai, R., Madhow, U., Suri, S.: Target tracking with binary proximity sensors: Fundamental limits, minimal descriptions, and algorithms. In: Proc. of ACM SenSys (2006)Google Scholar
  15. 15.
    Singh, J., Madhow, U., Kumar, R., Suri, S., Cagley, R.: Tracking multiple targets using binary proximity sensors. In: IPSN 2007: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 529–538. ACM, New York (2007)Google Scholar
  16. 16.
    Kasetkasem, T., Varsheny, P.K.: Communications structure planning for multisensor detection systems. In: IEE Proc. Radar, Sonar and Navigation, vol. 148, pp. 2–8 (February 2001)Google Scholar
  17. 17.
    Tong, L., Soon, V., Liu, Y.F.H.R.: Indeterminacy and identifiability of blind identification. IEEE Transactions 38, 499–509 (1991)zbMATHGoogle Scholar
  18. 18.
    Vikram, A.: Tracking in wireless sensor network using blind source separation algorithms. Master’s thesis, Cleveland State University, Cleveland, OH, USA (2010),
  19. 19.
    Zhang, H., Hou, J.: Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor Wireless Networks 1(1-2) (2005),
  20. 20.
    Zou, Y., Chakrabarty, K.: Sensor deployment and target localization in distributed sensor networks. Trans. on Embedded Computing Sys. 3(1), 61–91 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ye Zhu
    • 1
  • Anil Vikram
    • 1
  • Huirong Fu
    • 2
  1. 1.Department of Electrical and Computer EngineeringCleveland State UniversityClevelandUSA
  2. 2.Department of Computer Science and EngineeringOakland UniversityRochesterUSA

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