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Sensor Placement for 3-Coverage with Minimum Separation Requirements

  • Jung-Eun Kim
  • Man-Ki Yoon
  • Junghee Han
  • Chang-Gun Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5067)

Abstract

Sensors have been increasingly used for many ubiquitous computing applications such as asset location monitoring, visual surveillance, and human motion tracking. In such applications, it is important to place sensors such that every point of the target area can be sensed by more than one sensor. Especially, many practical applications require 3-coverage for triangulation, 3D hull building, and etc. Also, in order to extract meaningful information from the data sensed by multiple sensors, those sensors need to be placed not too close to each other—minimum separation requirement. To address the 3-coverage problem with the minimum separation requirement, this paper proposes two methods, so called, overlaying method and TRE-based method, which complement each other depending on the minimum separation requirement. For these two methods, we also provide mathematical analysis that can clearly guide us when to use the TRE-based method and when to use the overlaying method and also how many sensors are required. To the best of our knowledge, this is the first work that systematically addresses the 3-coverage problem with the minimum separation requirement.

Keywords

sensor placement 3-coverage minimum separation requirement coverage redundancy 

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References

  1. 1.
    Kershner, R.: The Number of Circles Covering a Set. American Journal of Mathematics 61(3), 665–671 (1939)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Tian, D., Georganas, N.D.: A Coverage-Preserving Node Scheduling Scheme for Large Wireless Sensor Networks. In: Proceedings of ACM Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 32–41 (2002)Google Scholar
  3. 3.
    Bai, X., Kumar, S., Yun, Z., Xuan, D., Lai, T.H.: Deploying wireless sensors to achieve both coverage and connectivity. In: Proceedings of ACM MobiHoc, pp. 131–142 (2006)Google Scholar
  4. 4.
    Esteban, C.H., Schmitt, F.: Multi-Stereo 3D Object Reconstruction. In: Proceedings of the first International Symposium on 3D Data Processing Visualization and Transmission (3DPVT), pp. 159–166 (2002)Google Scholar
  5. 5.
    Crossbow: MCS Cricket Series (MCS410), http://www.xbow.com
  6. 6.
    Chakrabarty, K., Iyengar, S.S., Qi, H., Cho, E.: Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks. IEEE Transactions on Computers 51(12), 1448–1453 (2002)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Wang, Y.C., Hu, C.C., Tseng, Y.C.: Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks. In: Proceedings of IEEE Wireless Internet Conference (WICON), pp. 114–121 (2005)Google Scholar
  8. 8.
    Iyengar, R., Kar, K., Banerjee, S.: Low-coordination topologies for redundancy in sensor networks. In: Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing(MobiHoc), pp. 332–342 (2005)Google Scholar
  9. 9.
    Huang, C.F., Tseng, Y.C.: The Coverage Problem in a Wireless Sensor Network. In: Proceedings of ACM Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 115–121 (2003)Google Scholar
  10. 10.
    Huang, C.F., Tseng, Y.C., Lo, L.C.: The Coverage Problem in Three-Dimensional Wireless Sensor Networks. In: Proceedings of IEEE GLOBECOM, pp. 3182–3186 (2004)Google Scholar
  11. 11.
    Yang, S., Dai, F., Cardei, M., Wu, J.: On Connected Multiple Point Coverage in Wireless Sensor Networks. Journal of Wireless Information Networks 13(4), 289–301 (2006)CrossRefGoogle Scholar
  12. 12.
    Hefeeda, M., Bagheri, M.: Randomized k-Coverage Algorithms for Dense Sensor Networks. In: Proceedings of IEEE INFOCOM, pp. 2376–2380 (2007)Google Scholar
  13. 13.
    Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., Gill, C.: Integrated coverage and connectivity configuration in wireless sensor networks. In: Proceedings of the 1st international conference on Embedded networked sensor systems(SenSys), pp. 28–39 (2003)Google Scholar
  14. 14.
    Xiaochun, X., Sartaj, S.: Approximation Algorithms for Sensor Deployment. IEEE Transactions on Computers 56(12), 1681–1695 (2007)CrossRefGoogle Scholar
  15. 15.
    Nam, M.Y., Al-Sabbagh, M.Z., Kim, J.E., Yoon, M.K., Lee, C.G., Ha, E.Y.: A Real-time Ubiquitous System for Assisted Living: Combined Scheduling of Sensing and Communication for Real-Time Tracking. IEEE Transactions on Computers (to appear, 2008)Google Scholar
  16. 16.
    Nam, M.Y., Al-Sabbagh, M.Z., Lee, C.G.: Real-Time Indoor Human/Object Tracking for Inexpensive Technology-Based Assisted Living. In: Proceedings of IEEE Real-Time Systems Symposium (RTSS) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jung-Eun Kim
    • 1
  • Man-Ki Yoon
    • 1
  • Junghee Han
    • 2
  • Chang-Gun Lee
    • 1
  1. 1.The School of Computer Science and EngineeringSeoul National UniversitySeoulKorea
  2. 2.Samsung Electronics Co. LtdSuwonKorea

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