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Optimum Detection Probability with Partially Controlled Random Deployment of Wireless Sensors with Mobile Base Stations

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 299)

Abstract

In this paper we analyze the problem of covering widely expanded field with wireless sensors where many of the known deployment and data aggregation methods become impractical. We deploy the wireless sensors in a partially controlled manner such that they are randomly placed on the lines of grid and mobile base stations like UAVs could be used to collect the data from the wireless sensors. Our objective is to maximize the detection probability of an event without overly deploying the sensors on the field. We have defined the detection probability to be the product of probability of an event been sensed and that data being collected by an UAV. Under this model, we analytically obtain a relationship between the grid spacing and a number of available UAVs which can maximize detection probability when two collaborative and independent strategies for UAVs and obtain some useful relationship in guiding design specification.

Keywords

Wireless sensor network Mobile base stations Large-scale network Optimum controlled random deployment 

References

  1. 1.
    Cordeiro, C., Agrawal, D.P.: Ad hoc and Sensor Networks. World Scientific Press, Singapore (2006)Google Scholar
  2. 2.
    Zhang, H., Hou, JC.: Is deterministic deployment worse than random deployment for wireless sensor networks? IEEE INFOCOM (2005)Google Scholar
  3. 3.
    Wang, Y., Agrawal, D.P.: Optimizing sensor networks for autonomous unmanned ground vehicles. Optics/Photonics in Security & Defence, pp. 15–18, Cardiff, UK (2008)Google Scholar
  4. 4.
    Clouqueur, T., Phipatanasuphorn, V., Ramanathan, P., Saluja, K.K.: Sensor Deployment Strategy for Target Detection Proceedings. ACM Workshop on Sensor Networks and Applications (WSNA), p. 428 (2002)Google Scholar
  5. 5.
    Tilak, S., Abu-Ghazaleh, N.B., Heinzelman, W.: Infrastructure Tradeoffs for Sensor Networks Proceedings. ACM Workshop on Sensor Networks and Applications (WSNA), p. 498 (2002)Google Scholar
  6. 6.
    Zou, Y., Chakrabarty, K.: Sensor Deployment and Target Localization Based on Virtual Forces Proceedings. IEEE Infocom (2003)Google Scholar
  7. 7.
    Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., Gill, C.: Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks, Proceedings. ACM SenSys, p. 289 (2003)Google Scholar
  8. 8.
    Ye, F., Zhong, G., Lu, S., Zhang, L.: PEAS: a Robust Energy Conserving Protocol for Long-Lived Sensor Networks, Proceedings. IEEE ICDCS, p. 287 (2003)Google Scholar
  9. 9.
    Zhang, H., Hou, J.: Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. NSF International Workshop on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks (2004)Google Scholar
  10. 10.
    Wang, W., Srinivasan, V., Chua, K.-C.: Using mobile relays to prolong the lifetime of wireless sensor networks. In MobiCom ‘05: Proceedings of the 11th annual international conference on Mobile computing and networking. ACM Press, Cologne, Germany (2005)Google Scholar
  11. 11.
    Liu, B., et al.: Mobility improves coverage of sensor networks. In Proceedings of the 6th ACM international Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc ‘05. Urbana-Champaign, ACM Press, IL, USA (2005)Google Scholar
  12. 12.
    Shah, R.C., et al.: Data MULEs: modeling a three-tier architecture for sparse sensor networks. In: Proceedings of the IEEE Workshop on Sensor Network Protocols and Applications (2003)Google Scholar
  13. 13.
    Bisnik, M., Abouzeid, A.A., Isler, A.A.: Stochastic event capture using mobile sensors subject to a quality metric. IEEE Trans Rob. 23(4), 676–692 (2007)Google Scholar
  14. 14.
    Leoncini, M., Resta, G., Santi, P.: Analysis of a wireless sensor dropping problem in wide-area environmental monitoring. In: Fourth International Symposium on Information Processing in Sensor Networks. IPSN 2005, pp. 239–245, 15 April 2005Google Scholar
  15. 15.
    Mathai, A.M.: An Introduction to Geometrical Probability: Distributional Aspects with Applications, 1st edn. CRC, Boca Raton (1999)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.OBR Center of Distributed and Mobile Computing, Department of Computer ScienceUniversity of CincinnatiCincinnatiUSA

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