skip to main content
article

Navigation protocols in sensor networks

Published:01 August 2005Publication History
Skip Abstract Section

Abstract

We develop distributed algorithms for adaptive sensor networks that respond to directing a target through a region of space. We model this problem as an online distributed motion planning problem. Each sensor node senses values in its perception space and has the ability to trigger exceptions events we call “danger” and model as “obstacles”. The danger/obstacle landscape changes over time. We present algorithms for computing distributed maps in perception space and for using these maps to compute adaptive paths for a mobile node that can interact with the sensor network. We give the analysis to the protocol and report on hardware experiments using a physical sensor network consisting of Mote sensors. We also show how to reduce searching space and communication cost using Voronoi diagram.

References

  1. Aslam, J., Li, Q., and Rus, D. 2003. Three power-aware routing algorithms for sensor networks. Wirel. Comm. Mobile Comput. 3, 2 (Mar.), 187--208.Google ScholarGoogle Scholar
  2. Batalin, M. and Sukhatme, G. 2003. Efficient exploration without localization. In the International Conference on Robotics and Automation (ICRA'03). Taipei,Taiwan.Google ScholarGoogle Scholar
  3. Batalin, M. A. and Sukhatme, G. S. 2004. Coverage, exploration and deployment by a mobile robot and communication network. Telecomm. Syst. J. 26, 2--4.Google ScholarGoogle Scholar
  4. Capkun, S., Hamdi, M., and Hubaux, J. P. 2002. GPS-free positioning in mobile ad-hoc networks. J. Cluster Comput. Google ScholarGoogle Scholar
  5. Cheng, X., Thaeler, A., Xue, G., and Chen, D. 2004. Tps: A time-based positioning scheme for outdoor sensor networks. In the 23rd Conference of the IEEE Communications Society (INFOCOM '04). Hong Kong.Google ScholarGoogle Scholar
  6. Corke, P., Peterson, R., and Rus, D. 2003. Networked robots: Flying robot navigation using a sensor net. In the 11th International Symposium of Robotics Research. Sienna, Italy.Google ScholarGoogle Scholar
  7. Estrin, D., Govindan, R., and Heidemann, J. 2000. Embedding the internet. Comm. ACM 43, 5 (May), 39--41. Google ScholarGoogle Scholar
  8. Fang, Q., Gao, J., and Guibas, L. 2004. Locating and bypassing routing holes in sensor networks. In the 23rd Conference of the IEEE Communications Society (INFOCOM'04). Hong Kong.Google ScholarGoogle Scholar
  9. Ganesan, D., Krishnamachari, B., Woo, A., Culler, D., Estrin, D., and Wicker, S. 2002. Complex behavior at scale: An experimental study of low-power wireless sensor networks. UCLA Computer Science Tech. Rep. UCLA/CSD-TR 02-0013.Google ScholarGoogle Scholar
  10. Guibas, L. J. and Stolfi, J. 1983. Primitives for the manipulation of general subdivisions and the computation of voronoi diagrams. In ACM Symposium on the Theory of Computing (STOC'83). 221--234. Google ScholarGoogle Scholar
  11. Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., and Pister, K. 2000. System architecture directions for network sensors. In Architectural Support for Programming Languages and Operating Systems (ASPLOS'00). Google ScholarGoogle Scholar
  12. Huang, Q., Lu, C., and Roman, C. 2003. Spatiotemporal multicast for sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar
  13. Intanagonwiwat, C., Govindan, R., and Estrin, D. 2000. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the Mobicom'00. Boston, MA. Google ScholarGoogle Scholar
  14. Koditschek, D. E. 1989. Planning and control via potential fuctions. Robotics Rev. I, 349--367. Google ScholarGoogle Scholar
  15. Latombe, J.-C. 1992. Robot Motion Planning. Kluwer, New York. Google ScholarGoogle Scholar
  16. Lengyel, J., Reichert, M., Donald, B., and Greenberg, D. 1990. Real-time robot motion planning using rasterizing computer graphics hardware. In Proceedings of SIGGRAPH. Dallas, TX, 327--336. Google ScholarGoogle Scholar
  17. Meguerdichian, S., Koushanfar, F., Qu, G., and Potkonjak, M. 2001. Exposure in wireless ad hoc sensor networks. In MOBICOM '01. Rome, Italy. 139--150. Google ScholarGoogle Scholar
  18. Moore, D., Leonard, J., Rus, D., and Teller, S. 2004. Robust distributed network localization with noisy range measurements. In ACM SenSys'04. Baltimore, MD. Google ScholarGoogle Scholar
  19. Niculescu, D. and Badrinath, B. R. 2003. Ad hoc positioning system (APS) using AOA. In the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '03). San Francisco, CA.Google ScholarGoogle Scholar
  20. Savvides, A., Han, C.-C., and Strivastava, M. B. 2001. Dynamic fine-grained localization in ad-hoc networks of sensors. In MOBICOM '01. Rome, Italy, 166--179. Google ScholarGoogle Scholar
  21. Sundaram, N. and Ramanathan, P. 2002. Connectivity based location estimation scheme for wireless ad hoc networks. In Proceedings of Globecom '02. Taipei, Taiwan.Google ScholarGoogle Scholar
  22. Takagi, H. and Kleinrock, L. 1984. Optimal transmission ranges for randomly distributed packet radio terminals. IEEE Trans. Comm. 32, 3 (Mar.).Google ScholarGoogle Scholar
  23. Veltri, G., Huang, Q., Qu, G., and Potkonjak, M. 2003. Minimal and maximal exposure path algorithms for wireless embedded sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar
  24. Wan, C.-Y., Campbell, A., and Krishnamurthy, L. 2002. PSFQ: A reliable transport protocol for wireless sensor networks. In the 1st ACM International Workshop on Wireless Sensor Networks and Applications. Atlanta, GA. Google ScholarGoogle Scholar
  25. Wan, C.-Y., Eisenman, S. B., and Campbell, A. T. 2003. Coda: Congestion detection and avoidance in sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar
  26. Woo, A., Tong, T., and Culler, D. 2003. Taming the underlying challenges of reliable multihop routing in sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar
  27. Yan, T., He, T., and Stankovic, J. A. 2003. Differentiated surveillance for sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar
  28. Ye, F., Luo, H., Cheng, J., Lu, S., and Zhang, L. 2002. A two-tier data dissemination model for large-scale wireless sensor networks. In ACM Mobicom '02. Atlanta, GA. Google ScholarGoogle Scholar
  29. Zhao, F., Shin, J., and Reich, J. 2002. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Process. Mag. 19, 2 (Mar.), 61--72.Google ScholarGoogle Scholar
  30. Zhao, J. and Govindan, R. 2003. Understanding packet delivery performance in dense wireless sensor networks. In ACM SenSys '03. Los Angeles, CA. Google ScholarGoogle Scholar

Index Terms

  1. Navigation protocols in sensor networks

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader