Declarative Support for Sensor Data Cleaning

  • Shawn R. Jeffery
  • Gustavo Alonso
  • Michael J. Franklin
  • Wei Hong
  • Jennifer Widom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3968)

Abstract

Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP’s effectiveness and ease of use through three real-world scenarios.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alien Technology. Nanoscanner Reader User GuideGoogle Scholar
  2. 2.
    Alien Technology. Personal correspondenceGoogle Scholar
  3. 3.
  4. 4.
    Abadi, D., et al.: Aurora: a data stream management system. In: SIGMOD (2003)Google Scholar
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Arasu, A., et al.: The CQL continuous query language: Semantic foundations and query execution. VLDB Journal (to appear)Google Scholar
  9. 9.
    Babcock, B., et al.: Models and issues in data stream systems. In: SIGMOD (2002)Google Scholar
  10. 10.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Buonadonna, P., et al.: TASK: Sensor Network in a Box. In: EWSN (2005)Google Scholar
  12. 12.
    Chandrasekaran, S., et al.: TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In: CIDR (2003)Google Scholar
  13. 13.
    Cooper, O., et al.: HiFi: A Unified Architecture for High Fan-in Systems. In: VLDB (2004)Google Scholar
  14. 14.
    Demand-response, http://dr.me.berkeley.edu/
  15. 15.
    Deshpande, A., et al.: Model-Driven Data Acquisition in Sensor Networks. In: VLDB Conference (2004)Google Scholar
  16. 16.
    Dey, A.K.: Providing Architectural Support for Building Context-Aware Applications. Ph.D. thesis, Georgia Institute of Technology (2000)Google Scholar
  17. 17.
    Elnahrawy, E., et al.: Cleaning and querying noisy sensors. In: WSNA 2003: Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications (2003)Google Scholar
  18. 18.
    Fishkin, K.P., Jiang, B., Philipose, M., Roy, S.: I Sense a Disturbance in the Force: Unobtrusive Detection of Interactions with RFID-tagged Objects. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 268–282. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Floerkemeier, C., Lampe, M.: Issues with RFID usage in ubiquitous computing applications. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 188–193. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Franklin, M.J., et al.: Design Considerations for High Fan-In Systems: The HiFi Approach. In: CIDR (2005)Google Scholar
  21. 21.
    Galhardas, H., et al.: Declarative data cleaning: Language, model, and algorithms. In: VLDB, pp. 371–380 (2001)Google Scholar
  22. 22.
    Gay, D., et al.: The nesC language: A holistic approach to networked embedded systems. In: SIGPLAN (2003)Google Scholar
  23. 23.
  24. 24.
  25. 25.
    Jeffery, S.R., et al.: A Pipelined Framework for Online Cleaning of Sensor Data Streams. In: ICDE (2006)Google Scholar
  26. 26.
    Kidd, C.D., et al.: The Aware Home: A Living Laboratory for Ubiquitous Computing Research. In: Cooperative Buildings, pp. 191–198 (1999)Google Scholar
  27. 27.
    Madden, S., et al.: The Design of an Acquisitional Query Processor For Sensor Networks. In: SIGMOD (2003)Google Scholar
  28. 28.
    Mukhopadhyay, S., et al.: Data aware, low cost error correction for wireless sensor networks. In: WCNC (2004)Google Scholar
  29. 29.
    Paskin, M.A., et al.: A robust architecture for distributed inference in sensor networks. In: IPSN (2005)Google Scholar
  30. 30.
    Philipose, M., et al.: Mapping and Localization with RFID Technology. Technical Report IRS-TR-03-014, Intel Research (December 2003)Google Scholar
  31. 31.
    Qin, S.: Neural networks for intelligent sensors and control — practical issues and some solutions. In: Neural Networks for Control (1996)Google Scholar
  32. 32.
    Rahm, E., et al.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)Google Scholar
  33. 33.
    Särndal, C.-E., et al.: Model Assisted Survey Sampling. Springer Series in Statistics. Springer, New York (1992)MATHGoogle Scholar
  34. 34.
    Sonoma Redwood Sensor Network Deployment, http://www.cs.berkeley.edu/~get/sonoma/
  35. 35.
    Tolle, G., et al.: A macrosope in the redwoods. In: SenSys (2005)Google Scholar
  36. 36.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shawn R. Jeffery
    • 1
  • Gustavo Alonso
    • 2
  • Michael J. Franklin
    • 1
  • Wei Hong
    • 3
  • Jennifer Widom
    • 4
  1. 1.UC Berkeley 
  2. 2.ETH Zurich 
  3. 3.Arched Rock Corporation 
  4. 4.Stanford University 

Personalised recommendations