research-article

A methodology for extracting temporal properties from sensor network data streams

Authors:

Dimitrios Lymberopoulos,

Athanasios Bamis,

Andreas SavvidesAuthors Info & Claims

MobiSys '09: Proceedings of the 7th international conference on Mobile systems, applications, and services

Pages 193 - 206

https://doi.org/10.1145/1555816.1555836

Published: 22 June 2009 Publication History

Abstract

The extraction of temporal characteristics from sensor data streams can reveal important properties about the sensed events. Knowledge of temporal characteristics in applications where sensed events tend to periodically repeat, can provide a great deal of information towards identifying patterns, building models and using the timing information to actuate and provide services. In this paper we outline a methodology for extracting the temporal properties, in terms of start time and duration, of sensor data streams that can be used in applications such as human, habitat, environmental and traffic monitoring where sensed events repeat over a time window. Its application is demonstrated on a 30-day dataset collected from one of our assisted living sensor network deployments.

References

[1]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases, pages 487--499, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.

Digital Library

[2]

R. Agrawal and R. Srikant. Mining sequential patterns. In ICDE '95: Proceedings of the Eleventh International Conference on Data Engineering, pages 3--14, Washington, DC, USA, 1995. IEEE Computer Society.

Digital Library

[3]

J. Coble, D. J. Cook, and L. B. Holder. Structure discovery in sequentially-connected data streams. International Journal on Artificial Intelligence Tools, 2006.

[4]

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1--38, 1977.

[5]

E. Heierman, M. Youngblood, and D. J. Cook. Mining temporal sequences to discover interesting patterns. In KDD Workshop on Mining Temporal and Sequential Data, 2004.

[6]

I. Jonyer, L. B. Holder, and D. J. Cook. Mdl-based context-free graph grammar induction and applications. International Journal on Artificial Intelligence Tools, 2004.

[7]

L. Liao, D. J. Patterson, D. Fox, and H. Kautz. Learning and inferring transportation routines. Artif. Intell., 171(5-6):311--331, 2007.

Digital Library

[8]

D. Lymberopoulos, A. Bamis, and A. Savvides. Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In Proceedings of the International Conference on PErvasive Technologies Related to Assistive Environments (PETRA), 2008.

Digital Library

[9]

D. Lymberopoulos, A. Ogale, A. Savvides, and Y. Aloimonos. A sensory grammar for inferring behaviors in sensor networks. In Proceedings of Information Processing in Sensor Networks (IPSN), April 2006.

Digital Library

[10]

D. Lymberopoulos, T. Teixeira, and A. Savvides. Detecting patterns for assisted living using sensor networks. In Proceedings of SensorComm, October 2007.

Digital Library

[11]

D. Lymberopoulos, T. Teixeira, and A. Savvides. Macroscopic human behavior interpretation using distributed imagers and other sensors. In to appear in Proceedings of IEEE, 2008.

[12]

J. B. Macqueen. Some methods of classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pages 281--297, 1967.

[13]

H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259--289, 1997.

Digital Library

[14]

J. R. Nayak and D. J. Cook. Approximate association rule mining. In Proceedings of FLAIRS Conference, 2001.

Digital Library

[15]

J. C. G. Ramirez, D. J. Cook, L. L. Peterson, and D. M. Peterson. An event set approach to sequence discovery in medical data. Intell. Data Anal., 2000.

[16]

K. Romer. Distributed mining of spatio-temporal event patterns in sensor networks. In EAWMS / DCOSS, 2006.

[17]

P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison-Wesley, 2006.

Digital Library

[18]

T. Teixeira and A. Savvides. Lightweight people counting and localizing in indoor spaces using camera sensor nodes. In ACM/IEEE International Conference on Distributed Smart Cameras, September 2007.

[19]

The BScope project. http://bscope.eng.yale.edu.

[20]

H. D. Vinod. Integer programming and the theory of grouping. Journal of the American Statistical Association, 64(326):506--519, June 1969.

[21]

A. Yu, A. Bamis, D. Lymberopoulos, T. Teixeira, and A. Savvides, editors. Proceedings of the International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems (UrbanSense08). Raleigh, North Carolina, Nov 2008.

[22]

Y. Zhao, G. Karypis, and U. Fayyad. Hierarchical clustering algorithms for document datasets. Data Min. Knowl. Discov., 10(2):141--168, 2005.

Digital Library

Cited By

(2018)Improving transmission delay with sink location in low-duty-cycle wireless sensor networksInternational Journal of Ad Hoc and Ubiquitous Computing10.1504/IJAHUC.2017.08513025:4(213-224)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1504/IJAHUC.2017.085130
Kim HNam DCheon S(2016)Determination of Representative Path Set from Vehicle Trajectory SamplesJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000052830:4Online publication date: Jul-2016
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000528
Lin YSsu KChiang HYang C(2013)Reducing Communication Delay by Finding Sink Location in Low-Duty-Cycle Wireless Sensor NetworksProceedings of the 2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing10.1109/PRDC.2013.30(136-137)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1109/PRDC.2013.30
Show More Cited By

Index Terms

A methodology for extracting temporal properties from sensor network data streams
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Recommendations

Empirical robustness analysis of wireless connectivity in sensor network deployments for assisted living at home
Telehealth '07: The Third IASTED International Conference on Telehealth

Healthcare and, in particular, telehealth applications are prospective areas for using low power, wireless sensors for remote monitoring. Elderly people can ask for remote assistance with the help of wireless sensor networks and stay at home with ...
An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how ...
Coverage-aware sensor engagement in dense sensor networks
Selected papers of EUC 2005

Wireless sensor networks are capable of carrying out surveillance missions for various applications in remote areas without human interventions. An essential issue of sensor networks is to search for the balance between the limited battery supply and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiSys '09: Proceedings of the 7th international conference on Mobile systems, applications, and services

June 2009

370 pages

ISBN:9781605585666

DOI:10.1145/1555816

Co-chair:
Krzysztof Zielinski
AGH University of Science and Technology, Poland
,
General Chair:
Adam Wolisz
Technische Universität Berlin, Germany
,
Program Chairs:
Jason Flinn
University of Michigan, USA
,
Anthony LaMarca
Intel Research Seattle, USA

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

Mobisys '09

Sponsor:

Mobisys '09: The 7th International Conference on Mobile Systems, Applications, and Services

June 22 - 25, 2009

Kraków, Poland

Acceptance Rates

Overall Acceptance Rate 274 of 1,679 submissions, 16%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
435
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

(2018)Improving transmission delay with sink location in low-duty-cycle wireless sensor networksInternational Journal of Ad Hoc and Ubiquitous Computing10.1504/IJAHUC.2017.08513025:4(213-224)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1504/IJAHUC.2017.085130
Kim HNam DCheon S(2016)Determination of Representative Path Set from Vehicle Trajectory SamplesJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000052830:4Online publication date: Jul-2016
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000528
Lin YSsu KChiang HYang C(2013)Reducing Communication Delay by Finding Sink Location in Low-Duty-Cycle Wireless Sensor NetworksProceedings of the 2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing10.1109/PRDC.2013.30(136-137)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1109/PRDC.2013.30
Hu LChen YWang SJia L(2013)A Nonintrusive and Single-Point Infrastructure-Mediated Sensing Approach for Water-Use Activity Recognition2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing10.1109/HPCC.and.EUC.2013.304(2120-2126)Online publication date: Nov-2013
https://doi.org/10.1109/HPCC.and.EUC.2013.304
Mao YMeghanathan NWozniak M(2012)Delay-bounded utility-based event detection in energy harvesting sensor networksProceedings of the Second International Conference on Computational Science, Engineering and Information Technology10.1145/2393216.2393335(711-716)Online publication date: 26-Oct-2012
https://dl.acm.org/doi/10.1145/2393216.2393335
Sung CFeldman DRus D(2012)Trajectory clustering for motion prediction2012 IEEE/RSJ International Conference on Intelligent Robots and Systems10.1109/IROS.2012.6386017(1547-1552)Online publication date: Oct-2012
https://doi.org/10.1109/IROS.2012.6386017
Yingchi Mao Weiming Ding (2012)Delay-bounded event detection in energy harvesting sensor networks2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM)10.1109/EEESym.2012.6258652(312-315)Online publication date: Jun-2012
https://doi.org/10.1109/EEESym.2012.6258652
Mao YYves AXu F(2012)Delay-Bounded Data Forwarding in Low-Duty-Cycle Sensor NetworksIntelligent Automation & Soft Computing10.1080/10798587.2012.1064328918:7(795-806)Online publication date: Jan-2012
https://doi.org/10.1080/10798587.2012.10643289
Bamis AFang JSavvides AHuang JKoudas NJones GWu XCollins-Thompson KAn A(2010)A method for discovering components of human rituals from streams of sensor dataProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871537(779-788)Online publication date: 26-Oct-2010
https://dl.acm.org/doi/10.1145/1871437.1871537
Bamis AFang JSavvides AAbdelzaher TVoigt TWolisz A(2010)Discovering routine events in sensor streams for macroscopic sensing compositionProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1145/1791212.1791278(408-409)Online publication date: 12-Apr-2010
https://dl.acm.org/doi/10.1145/1791212.1791278
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents