skip to main content
10.1145/1579114.1579119acmotherconferencesArticle/Chapter ViewAbstractPublication PagespetraConference Proceedingsconference-collections
research-article

STFL: a spatio temporal filtering language with applications in assisted living

Published:09 June 2009Publication History

ABSTRACT

In this paper we introduce the Spatio Temporal Filtering Language (STFL), which is a language framework that aims to provide the primitives for easily defining rules and sequences of rules and constraints. These sequences of rules can be used to convert low-level streams of sensor data into higher-level semantics and provide triggers for actuation. Among others STFL provides support for heterogeneous types of sensors, composability and code reusability. Special emphasis is given on the support of different categories of users by providing different types of interfaces spanning from a natural-like language aiming at end-users to a regular scripting language aiming at system developers. The expressiveness and power of STFL is presented through an assisted living scenario.

References

  1. D. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, et al. The Design of the Borealis Stream Processing Engine. In Second Biennial Conference on Innovative Data Systems Research (CIDR 2005), Asilomar, CA, January, 2005.Google ScholarGoogle Scholar
  2. R. Balani, A. Singhania, S. Han, R. Rengaswamy, and M. B. Srivastava. Vire: Virtual reconfiguration framework for embedded processing in distributed image sensors, January - April 2007. NESL, UCLA Technical Report TR-UCLA-NESL-200701-01.Google ScholarGoogle Scholar
  3. A. Bamis, D. Lymberopoulos, T. Teixeira, and A. Savvides. Towards precision monitoring of elders for providing assistive services. In PETRA '08: Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments, pages 1--8, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Lymberopoulos, A. Bamis, and A. Savvides. Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In PETRA '08: Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments, pages 1--8, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Lymberopoulos, A. Bamis, and A. Savvides. A methodology for extracting temporal properties from sensor network data streams. In Proceedings of the 7th ACM/Usenix International Conference on Mobile Systems, Applications and Services (MobiSys '09), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Lymberopoulos, T. Teixeira, and A. Savvides. Macroscopic human behavior interpretation using distributed imager and other sensors. Proceedings of the IEEE, 96(10):1657--1677, Oct. 2008.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Madden, M. Franklin, J. Hellerstein, and W. Hong. TinyDB: an acquisitional query processing system for sensor networks. ACM Transactions on Database Systems (TODS), 30(1):122--173, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. Mainland, M. Welsh, and G. Morrisett. Flask: A language for data-driven sensor network programs, May 2006. Harvard University Technical Report TR-13-06.Google ScholarGoogle Scholar
  9. R. Newton, G. Morrisett, and M. Welsh. The regiment macroprogramming system. In Proceedings of the 6th international conference on Information processing in sensor networks, pages 489--498. ACM Press New York, NY, USA, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Pigot, A. Mayers, and S. Giroux. The intelligent habitat and everyday life activity support. In Proc. of the 5th International conference on Simulations in Biomedicine, April, pages 2--4.Google ScholarGoogle Scholar
  11. R. Sugihara and R. Gupta. Programming models for sensor networks: A survey, January 2007. UCSD Technical Report CS2007-0881.Google ScholarGoogle Scholar
  12. M. Welsh and G. Mainland. Programming sensor networks using abstract regions. In First USENIX/ACM Symposium on Networked Systems Design and Implementation (NSDI '04), March 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. Whitehouse, C. Sharp, E. Brewer, and D. Culler. Hood: a neighborhood abstraction for sensor networks. In MobiSys '04: Proceedings of the 2nd international conference on Mobile systems, applications, and services, pages 99--110, New York, NY, USA, 2004. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. K. Whitehouse, F. Zhao, and J. Liu. Semantic Streams: A Framework for Composable Semantic Interpretation of Sensor Data. LECTURE NOTES IN COMPUTER SCIENCE, 3868:5, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Yao and J. Gehrke. The Cougar Approach to In-Network Query Processing in Sensor Networks. SIGMOD RECORD, 31(3):9--18, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. S. Yu, A. Bamis, D. Lymberopoulos, T. Teixeira, and A. Savvides. Personalized awareness and safety with mobile phones as sources and sinks. In International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems (UrbanSense08), 2008.Google ScholarGoogle Scholar

Index Terms

  1. STFL: a spatio temporal filtering language with applications in assisted living

    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
    • Published in

      cover image ACM Other conferences
      PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
      June 2009
      481 pages
      ISBN:9781605584096
      DOI:10.1145/1579114

      Copyright © 2009 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 9 June 2009

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader