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Spatio-temporal Context in Wireless Sensor Networks

  • Anahit Martirosyan
  • Azzedine Boukerche
Chapter
Part of the Monographs in Theoretical Computer Science. An EATCS Series book series (EATCS)

Abstract

Context represents any knowledge obtained from Wireless Sensor Networks (WSNs) regarding the object being monitored. Context-awareness is an important feature of WSN applications as it provides an ultimate tool for making the applications “smart”. The information about a sensed in a WSN phenomenon is comprehensive only when it includes the geographical location and the time of occurrence of the phenomenon. Thus, the location and time are essential constituents of WSNs’ context, though the concept of context is not limited to only space and time. In this chapter, we consider the spatio-temporal context of WSNs as it serves as a foundation for context-aware systems. In order to build context-aware WSNs it is necessary to consider three areas concerning the spatio-temporal correlation of events sensed in WSNs: node localization, temporal event ordering and time synchronization. While localization’s task is to provide geographic coordinates of a sensed event, preserving temporal relationships of the events in WSNs is necessary for ensuring their correct interpretation at the monitoring centre and for taking proper and prompt actions. The latter can be achieved by guaranteeing time synchronization and temporal event ordering mechanisms. We present an overview of selected algorithms in each of the areas, first providing the necessary background and then presenting a comparison of features of the discussed algorithms.

Keywords

Global Position System Cluster Head Time Synchronization Receive Signal Strength Indication Unknown Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    J. Albowitz, A. Chen, and L. Zhang. Recursive position estimation in sensor networks. In Proceedings of IEEE ICNP, pages 35–41, 2001.Google Scholar
  2. 2.
    P. Bahl, and V. N. Padmanabhan. Radar: An in-building RF-based user location and tracking system. In Proceedings of IEEE Infocom, 775–784, 2000.Google Scholar
  3. 3.
    A. Boukerche. Algorithms and protocols for wireless sensor networks. Wiley Series on Parallel and Distributed Computing, 2008.Google Scholar
  4. 4.
    A. Boukerche, H. Oliveira, E. Nakamura, A. Loureiro. Vehicular ad hoc networks: a new challenge for localization-based systems. Computer Communications 31(12):2838–2849, 2008.CrossRefGoogle Scholar
  5. 5.
    A. Boukerche, and A. Martirosyan. An efficient algorithm for preserving events’ temporal relationships in wireless sensor actor networks. In Proceedings of the 32nd IEEE Conference on Local Computer Networks, pages 771–780, 2007.Google Scholar
  6. 6.
    A. Boukerche, and A. Martirosyan. An energy-aware and fault tolerant inter-cluster communication based protocol for wireless sensor networks. In Proceedings of the 50th IEEE Global Telecommunications Conference, pages 1164–1168, 2007.Google Scholar
  7. 7.
    A. Boukerche, F. H. S. Silva, R. B. Araujo, R. W. N. Pazzi. A low latency and energy aware event ordering algorithm for wireless actor and sensor networks. In Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 111–117, 2005.Google Scholar
  8. 8.
    A. Boukerche, R. W. N. Pazzi, and R. B. Araujo. A fast and reliable protocol for wireless sensor networks in critical conditions monitoring applications. In Proceedings of 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 157–164, 2004.Google Scholar
  9. 9.
    H. Chan, M. Luk, and A. Perrig. Using clustering information for sensor network localization. Lecture Notes in Computer Science, Springer, Berlin, pages 109–125, 2005.Google Scholar
  10. 10.
    A. Dey. Understanding and using context. Personal and Ubiquitous Computing, 5(1):4–7, 2001.CrossRefGoogle Scholar
  11. 11.
    J. Elson, K. Romer. Wireless sensor networks: a new regime for time synchronization. ACM SIGCOMM, Computer Communication Review, 33(1):149–154, 2003.CrossRefGoogle Scholar
  12. 12.
    J. Elson. Time Synchronization for Wireless Sensor Networks, PhD Thesis, University of California, Los Angeles, 2003.Google Scholar
  13. 13.
    S. Ganeriwal, R. Kumar, M. B. Srivastava. Timing-sync Protocol for Sensor Networks. In Proceedings of the 1st International Conference on Embedded networked sensor systems, pages 138–149, 2003.Google Scholar
  14. 14.
    J. L. Girod and D. Estrin. Fine-grained network time synchronization using reference broadcasts. In Proceedings of the Fifth Symposium on Operating systems Design and Implementation, pages 147–163, 2002.Google Scholar
  15. 15.
    L. Girod and D. Estrin. Robust range estimation using acoustic and multimodal sensing. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1312–1320, 2001.Google Scholar
  16. 16.
    G. H. Golub and C. F. Van Loan. Matrix Computations, 3rd edition, Johns Hopkins University Press, 1996.Google Scholar
  17. 17.
    J. Y. Halpern and I. Suzuki. Clock synchronization and the Power of Broadcasting. Distributed Computing, 5(2):73–82, 1991.zbMATHCrossRefGoogle Scholar
  18. 18.
    R. Hayton. OASIS: An Open Architecture for Secure Interworking Services, PhD Thesis, University of Cambridge, Cambridge, 1996.Google Scholar
  19. 19.
    J. Hightower and G. Borriello. Location systems for ubiquitous computing, Computer, 34(8):57–66, 2001.CrossRefGoogle Scholar
  20. 20.
    W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocols for wireless sensor networks. In Proceedings of the 33rd Annual International Conference on System Sciences, pages 3005–3014, 2000.Google Scholar
  21. 21.
    R. Jain. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, Wiley, New York, 1991.zbMATHGoogle Scholar
  22. 22.
    H. Kopetz, and W. Schwabl. Global Time in Distributed Real-Time Systems. Technische Universitat Wien, 1989.Google Scholar
  23. 23.
    H. Kopetz, and W. Ochsenreiter. Clock synchronization in distributed real-time systems. IEEE Transactions on Computers, C-36(8):933–939, 1987.CrossRefGoogle Scholar
  24. 24.
    D. Koutsonikolas, S. M. Das, Y. C. Hu. Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13):2577–2592, 2007.CrossRefGoogle Scholar
  25. 25.
    L. Lamport. Time, clocks, and the ordering of events in a distributed system. Communications of the ACM, 54(3):558–565, 1978.CrossRefGoogle Scholar
  26. 26.
    M. Mansouri-Samani, M. Sloman. GEM a generalized event monitoring language for distributed systems. IEE/IOP/BCS Distributed Systems Engineering Journal, 4(25):96–108, 1997.CrossRefGoogle Scholar
  27. 27.
    M. Maroti, B. Kusy, G. Simon, and A. Ledeszi. The flooding time synchronization protocol. In ACM Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pages 39–49, 2004.Google Scholar
  28. 28.
    A. Martirosyan, and A. Boukerche. A lightweight iterative positioning algorithm for context-aware wireless sensor networks. In Proceedings of IEEE Globecom, 2009.Google Scholar
  29. 29.
    D. L. Mills. Internet time synchronization: the network time protocol. In Yang Z. and Anthony T. Marsland, editors, Global States and Time in Distributed Systems. IEEE Computer Society Press, 1994.Google Scholar
  30. 30.
    D. Niculescu and B. Nath. Ad hoc positioning system. In Proceedings of the IEEE International Conference on Global communications (GlobeCom ’ 01), pages 2926–2931, 2001.Google Scholar
  31. 31.
    B. Parkinson and J. Spilker. Global Positioning System: Theory and Application. American Institute for Aeronautics and Astronautics, 1996.Google Scholar
  32. 32.
    N. B. Priyantha, H. Balakrishnan, E. Demaine, and S. Teller. Mobile-assisted localization in wireless sensor networks. In IEEE INFOCOM, pages 172–183, 2005.Google Scholar
  33. 33.
    M. Raynal, A. Schiper, S. Toueg. The causal ordering abstraction and a simple way to implement it. Information Processing Letters archive, 39(6):343–350, 1991.zbMATHCrossRefMathSciNetGoogle Scholar
  34. 34.
    K. Römer, P. Blum, L. Meier. Time synchronization and calibration in wireless sensor networks, In Stojmenovic I., editor, Handbook of Sensor Networks: Algorithms and Architectures, Wiley, New York, NY, pages 199–237, 2005.Google Scholar
  35. 35.
    K. Römer. Temporal message ordering in wireless sensor networks. IFIP Mediterranean Workshop on Ad-Hoc Networks, pages 131–142, 2003.Google Scholar
  36. 36.
    K. Römer. Time synchronization in ad hoc networks. In ACM Symposium on Mobile Adhoc Networking and Computing, pages 0–1, 2001.Google Scholar
  37. 37.
    C. Savarese, J. M. Rabaey, K. Langendoen. Robust positioning algorithms for distributed ad-hoc wireless sensor networks, In Proceedings of the General Track: 2002 USENIX Annual, pages 317–327, 2002.Google Scholar
  38. 38.
    C. Savarese, J. M. Rabaey, and J. Beutel. Locationing in distributed ad-hoc wireless sensor networks. In ICASSP, pages 2037–2040, 2001.Google Scholar
  39. 39.
    A. Savvides, H. Park, and M. Srivastava. The n-Hop multilateration primitive for node localization problems, Mobile Networks and Applications, 8:443–451, 2003.CrossRefGoogle Scholar
  40. 40.
    A. Savvides, C. Han, M. B. Strivastava. Dynamic fine-grained localization in Ad-Hoc networks of sensors, In Proceedings of the 7th annual International Conference on Mobile computing and networking, pages 166–179, 2001.Google Scholar
  41. 41.
    B., Schilit, N., Adams, R.: Want, Context-Aware Computing Applications. In Proceedings of the First International Workshop on Mobile Computing Systems and Applications, pages 85–90, 1994.Google Scholar
  42. 42.
    Y. Shang, W. Ruml, Y. Zhang, M. Fromherz. Localization from connectivity in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 15(11):961–974, 2004.CrossRefGoogle Scholar
  43. 43.
    Y. C. Shim and C. V. Ramamoorthy. Monitoring and control of distributed systems. In Proceedings of the 1st International Conference of Systems Integration, pages 672–681, 1990.Google Scholar
  44. 44.
    M. L. Sichitiu and V. Ramadurai. Localization of wireless sensor networks with a mobile beacon. Technical Report. TR-03/06 Center for Advances Computing Communications, North Carolina State University, 2003.Google Scholar
  45. 45.
    I. Stojmenovic. Handbook of Sensor Networks, Algorithms and Architectures, Wiley, New York, NY, 2005.Google Scholar
  46. 46.
    R. Stoleru, J. A. Stankovic, S. H. Son. Robust node localization for wireless sensor networks. In Proceedings of the 4th workshop on Embedded networked sensors, 2007.Google Scholar
  47. 47.
    K.-F. Ssu, C.-H. Ou, and H. C. Jiau. Localization with mobile anchor points in wireless sensor networks. IEEE Transactions on Vehicular Technology, 54(3):1187–1197, 2005.CrossRefGoogle Scholar
  48. 48.
    K. Whitehouse, D. Culler. Calibration as parameter estimation in sensor networks. In Proceedings of ACM International Workshop on Wireless Sensor Networks and Applications, pages 59–67, 2002.Google Scholar
  49. 49.
    B. Xiao, H. Chen, and S. Zhou. A walking beacon-assisted localization in wireless sensor networks. Proceedings of the IEEE International Conference on Communications (ICC ’07), pages 3070–3075, 2007.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.University of OttawaOttawaCanada
  2. 2.University of OttawaOttawaCanada

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