Skip to main content
SpringerLink
Log in

Sensory Data Monitoring

  • Chapter
Learning from Data Streams

  • 1355 Accesses

Abstract

The goal of sensory data monitoring is to maximise the quality of data gathered by a sensor network. The principal problems for this task are, specifiying which data is most relevant to user’s goals, minimising the cost of gathering that data, and clearing the gathered data. This chapter outlines the state-of-the-art in addressing each of these challenges.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Adi, O. Etzion, AMIT—the situation manager. The VLDB Journal, 13(2):177–203, 2004.

    Article  Google Scholar 

  2. A. Bakshi, V.K. Prasanna, J. Reich, D. Larner, The abstract task graph: a methodology for architecture-independent programming of networked sensor systems. In: Proceedings of the 2005 Workshop on End-to-End, Sense-and-Respond Systems, Applications and Services (EESR ’05), pp. 19–24, Berkeley, CA, USA. USENIX Association, 2005.

    Google Scholar 

  3. M.A. Batalin, M. Rahimi, Y. Yu, D. Liu, A. Kansal, G.S. Sukhatme, W.J. Kaiser, M. Hansen, G.J. Pottie, M. Srivastava, D. Estrin, Call and response: experiments in sampling the environment. In: SenSys ’04: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 25–38, New York, NY, USA. ACM Press, 2004.

    Google Scholar 

  4. R. Beckwith, D. Teibel, P. Bowen, Report from the field: results from an agricultural wireless sensor network. In: First IEEE Workshop on Embedded Networked Sensors (EmNets), 2004.

    Google Scholar 

  5. P. Bonnet, J. Gehrke, P. Seshadri, Querying the physical world. IEEE personal communications, 7:10–15, 2000.

    Article  Google Scholar 

  6. A. Boulis, C.-C. Han, M.B. Srivastava, Design and implementation of a framework for programmable and efficient sensor networks. In: The First International Conference on Mobile Systems, Applications, and Services, MobiSys 2003, San Francisco, CA, 2003.

    Google Scholar 

  7. P. Buonadonna, D. Gay, J.M. Hellerstein, W. Hong, S. Madden, TASK: sensor network in a box. In: Proceedings of the Second European Workshop on Wireless Sensor Networks, pp. 133–144, 2005.

    Google Scholar 

  8. J. Burrell, T. Brooke, R. Beckwith, Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Computing 3(1):38–45, 2004.

    Article  Google Scholar 

  9. V. Byckovskiy, S. Megerian, D. Estrin, M. Potkonjak, A collaborative approach to in-place sensor calibration. In: Proceedings of the Second International Workshop on Information Processing in Sensor Networks (IPSN). Lecture Notes in Computer Science, vol. 2634, pp. 301–316. Springer, Berlin, 2003.

    Google Scholar 

  10. R. Cardell-Oliver, ROPE: a reactive, opportunistic protocol for environment monitoring sensor networks. In: EmNetS-II. The Second IEEE Workshop on Embedded Networked Sensors, Sydney, pp. 63–70, May 2005.

    Google Scholar 

  11. R. Cardell-Oliver, M. Reynolds, M. Kranz, A space and time requirements logic for sensor networks. In: Proceedings of IEEE-ISoLA 2006, to appear, 2007.

    Google Scholar 

  12. R. Cardell-Oliver, K. Smettem, M. Kranz, K. Mayer, A reactive soil moisture sensor network: design and field evaluation. International Journal of Distributed Sensor Networks, 149–162, 2005.

    Google Scholar 

  13. Decagon Echo-20 dielectric aquameter, 2005. [Online] Available at http://www.decagon.com/echo/.

  14. A. Deshpande, C. Guestrin, S.R. Madden, J.M. Hellerstein, W. Hong, Model-based approximate querying in sensor networks. The International Journal on Very Large Data Bases, 417–433, 2005.

    Google Scholar 

  15. E. Elnahrawy, B. Nath, Cleaning and querying noisy sensors. In: WSNA 03: Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, New York, NY, USA, pp. 78–87. ACM Press, 2003.

    Google Scholar 

  16. C.-L. Fok, G.-C. Roman, C. Lu, Rapid development and flexible deployment of adaptive wireless sensor network applications. In: Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’05), pp. 653–662. IEEE, June 2005.

    Google Scholar 

  17. C. Frank, K. Romer, Algorithms for generic role assignment in wireless sensor networks. In: Proceedings of the 3rd ACM Conference on Embedded Networked Sensor Systems (SenSys’05), November 2005.

    Google Scholar 

  18. R. Gummadi, O. Gnawali, R. Govindan, Macro-programming wireless sensor networks using Kairos. In: Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS), 2005.

    Google Scholar 

  19. G. Hackmann, C.-L. Fok, G.-C. Roman, C. Lu, Agimone: middleware support for seamless integration of sensor and IP networks. In: Lecture Notes in Computer Science, vol. 4026, pp. 101–118, 2006.

    Google Scholar 

  20. J.M. Hellerstein, W. Hong, S. Madden, K. Stanek, Beyond Average: Towards Sophisticated Sensing with Queries. In: 2nd International Workshop on Information Processing in Sensor Networks, IPSN ’03, March 2003.

    Google Scholar 

  21. C. Jaikaeo, C. Srisathapornphat, C.-C. Shen, Querying and tasking in sensor networks. In: SPIE’s 14th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Control (Digitization of the Battlespace V), Orlando, Florida, 24–28 April 2000.

    Google Scholar 

  22. S.R. Jeffery, G. Alonso, M.J. Franklin, W. Hong, J. Widom, A pipelined framework for online cleaning of sensor data streams. In: ICDE ’06: Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), p. 140, Washington, DC, USA. IEEE Computer Society, 2006.

    Google Scholar 

  23. M. Kranz, SENSID: a situation detector for sensor networks. Honours Thesis, June 2005. Honours Thesis, School of Computer Science and Software Engineering, University of Western Australia.

    Google Scholar 

  24. R. Kumar, M. Wolenetz, B. Agarwalla, J. Shin, P. Hutto, A. Paul, U. Ramachandran, Dfuse: a framework for distributed data fusion. In: Proceedings of the First International Conference on Embedded Networked Sensor Systems (SenSys), pp. 114–125. ACM Press, 2003.

    Google Scholar 

  25. N.G. Leveson, SAFEWARE: System Safety and Computers. Addison–Wesley, Reading, 1995.

    Google Scholar 

  26. P. Levis, D. Culler, Maté: A tiny virtual machine for sensor networks. In: Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS X), 2002.

    Google Scholar 

  27. S. Li, Y. Lin, S.H. Son, J.A. Stankovic, Y. Wei, Event detection using data service middleware in distributed sensor networks. Wireless Sensor Networks of Telecommunications Systems, 26:351–368, 2004.

    Google Scholar 

  28. L. Luo, T.F. Abdelzaher, T. He, J.A. Stankovic, EnviroSuite: an environmentally immersive programming framework for sensor networks. Transactions on Embedded Computing Systems, 5(3):543–576, 2006.

    Article  Google Scholar 

  29. S. Madden, M.J. Franklin, J.M. Hellerstein, W. Hong, TAG: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Operating Systems Review, 36(SI):131–146, 2002.

    Article  Google Scholar 

  30. S. Madden, M.J. Franklin, J.M. Hellerstein, W. Hong, The design of an acquisitional query processor for sensor networks. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD ’03), pp. 491–502, 2003.

    Google Scholar 

  31. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, J. Anderson, Wireless sensor networks for habitat monitoring. In: Proc. First ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, Georgia, USA, September 2002.

    Google Scholar 

  32. J. Meston, Efficiency and robustness in the gathering of data in wireless sensor networks: making every bit count. Honours Thesis, School of Computer Science and Software Engineering, University of Western Australia, November 2005.

    Google Scholar 

  33. M. Moss, Evaluation of event-aware environmental data compression schemes for wireless sensor networks. Honours Thesis, School of Computer Science and Software Engineering, University of Western Australia, November 2005.

    Google Scholar 

  34. D. Mukhopadhyay, S. Panigrahi, S. Dey, Data aware, low cost error correction for wireless sensor networks. In: Wireless Communications and Networking Conference, vol. 4, pp. 2492–2497, March 2004.

    Google Scholar 

  35. C. Rye, Development of a web-based interface for analysis of environmental data from a wireless sensor networks. Honours Thesis, School of Environmental Engineering, University of Western Australia, October 2005.

    Google Scholar 

  36. T. Schoellhammer, B. Greenstein, E. Osterweil, M. Wimbrow, D. Estrin, Lightweight temporal compression of microclimate datasets. In: Proceedings of 29th Annual IEEE International Conference on Local Computer Networks, pp. 516–524. IEEE, 2004.

    Google Scholar 

  37. S. Sen, R. Cardell-Oliver, A rule-based language for programming wireless sensor actuator networks using frequency and communication. In: Proceedings of the third IEEE Workshop on Embedded Networked Sensors (EMNETS’06), June 2006.

    Google Scholar 

  38. C. Srisathapornphat, C. Jaikaeo, C.-C. Shen, Sensor information networking architecture. In: 2000 International Workshop on Parallel Processing, ICPP 2000, Toronto, Canada, August 2000.

    Google Scholar 

  39. R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, D. Culler, An analysis of a large scale habitat monitoring application. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 214–226. ACM Press, 2004.

    Google Scholar 

  40. R. Szewczyk, J. Polastre, A. Mainwaring, D. Culler, Lessons from a sensor network expedition. In: Proceedings of the First European Workshop on Wireless Sensor Networks, Berlin, Germany, January 2004.

    Google Scholar 

  41. G. Tolle, D. Culler, Design of an application-cooperative management system for wireless sensor networks. In: Second European Workshop on Wireless Sensor Networks (EWSN), Istanbul, Turkey, January 2005.

    Google Scholar 

  42. M. Turon, MOTE-VIEW: a sensor network monitoring and management tool. In: EmNetS-II. The Second IEEE Workshop on Embedded Networked Sensors, Sydney, pp. 11–18, May 2005.

    Google Scholar 

  43. D. Wang, E. Arens, T. Webster, M. Shi, How the number and placement of sensors controlling room air distribution systems affect energy use and comfort. In: International Conference for Enhanced Building Operations, ICEBO 2002, Richardson, Texas, USA, October 2002.

    Google Scholar 

  44. M. Welsh, G. Mainland, Programming sensor networks using abstract regions. In: Proceedings of the First USENIX/ACM Symposium on Networked Systems Design and Implementation, NSDI ’04, March 2004.

    Google Scholar 

  45. K. Whitehouse, D. Culler, Macro-calibration in sensor/actuator networks. Mobile Networks and Applications, 8(4):463–472, 2003.

    Article  Google Scholar 

  46. W. Zhenhua, X. Hongbo, T. Yan, T. Jinwen, L.L. Jian, Integer Haar wavelet for remote sensing image compression. In: 6th International Conference on Signal Processing, August 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science & Software Engineering and Cooperative Research Centre for Plant-based Management of Dryland Salinity, The University of Western Australia, Perth, Australia

    Rachel Cardell-Oliver

Authors
  1. Rachel Cardell-Oliver
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rachel Cardell-Oliver .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, INESC-Porto LA and Faculty of Economics, University of Porto, Rua de Ceuta, 118, 6º, 4050-190, Porto, Portugal

    João Gama

  2. Tasmanian ICT Centre, GPO Box 1538, Hobart, TAS 7001, Australia

    Mohamed Medhat Gaber

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cardell-Oliver, R. (2007). Sensory Data Monitoring. In: Gama, J., Gaber, M.M. (eds) Learning from Data Streams. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-73679-4_8

Download citation

  • DOI: https://doi.org/10.1007/3-540-73679-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73678-3

  • Online ISBN: 978-3-540-73679-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation