skip to main content
10.1145/1807167.1807198acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Processing continuous join queries in sensor networks: a filtering approach

Published: 06 June 2010 Publication History

Abstract

While join processing in wireless sensor networks has received a lot of attention recently, current solutions do not work well for continuous queries. In those networks however, continuous queries are the rule. To minimize the communication costs of join processing, it is important to not ship non-joining tuples. In order to know which tuples do not join, prior work has proposed a precomputation step. For continuous queries however, repeating the precomputation for each execution is unnecessary and leaves aside that data tends to be temporally correlated. In this paper, we present a filtering approach for the processing of continuous join queries. We propose to keep the filters and to maintain them. The problems are determining the sizes of the filters and deciding which filters to update. Simplistic approaches result in bad performance. We show how to compute solutions that are optimal. Experiments on real-world sensor data indicate that our method performs close to a theoretical optimum and consistently outperforms state-of-the-art join approaches.

References

[1]
D. J. Abadi, S. Madden, and W. Lindner. REED: Robust, Efficient Filtering and Event Detection in Sensor Networks. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB '05), pages 769--780, Aug. 2005.
[2]
B. J. Bonfils and P. Bonnet. Adaptive and Decentralized Operator Placement for In-Network Query Processing. Telecommunication Systems, 26:389--409, June 2004.
[3]
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
[4]
V. Chowdhary and H. Gupta. Communication-Efficient Implementation of Join in Sensor Networks. In Proceedings of the 10th International Conference on Database Systems for Advanced Applications (DASFAA '05), pages 447--460, Apr. 2005.
[5]
A. Coman and M. A. Nascimento. A Distributed Algorithm for Joins in Sensor Networks. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management (SSDBM '07), page 27, July 2007.
[6]
A. Coman, M. A. Nascimento, and J. Sander. On Join Location in Sensor Networks. In Proceedings of the 2007 International Conference on Mobile Data Management (MDM '07), pages 190--197, May 2007.
[7]
A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong. Model-Driven Data Acquisition in Sensor Networks. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB '04), pages 588--599, Sept. 2004.
[8]
H. Hindi. A Tutorial on Convex Optimization II: Duality and Interior Point Methods. In Proceedings of the American Control Conference (ACC '06), June 2006.
[9]
A. Jain, E. Y. Chang, and Y.-F. Wang. Adaptive Stream Resource Management Using Kalman Filters. In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD '04), pages 11--22, June 2004.
[10]
N. Jain, D. Kit, P. Mahajan, P. Yalagandula, M. Dahlin, and Y. Zhang. STAR: Self-Tuning Aggregation for Scalable Monitoring. In Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB '07), pages 962--973, Sept. 2007.
[11]
R. A. Johnson and D. W. Wichern. Applied Multivariate Statistical Analysis. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, fifth edition, 2002.
[12]
S. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong. TinyDB: An Acquisitional Query Processing System for Sensor Networks. ACM Transactions on Database Systems (TODS), 30(1):122--173, March 2005.
[13]
J. Nocedal and S. J. Wright. Numerical Optimization. Springer Verlag, second edition, 2006.
[14]
C. Olston, J. Jiang, and J. Widom. Adaptive Filters for Continuous Queries Over Distributed Data Streams. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD '03), pages 563--574, June 2003.
[15]
C. Olston, B. T. Loo, and J. Widom. Adaptive Precision Setting for Cached Approximate Values. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIGMOD '01), pages 355--366, May 2001.
[16]
M. T. Özsu and P. Valduriez. Principles of Distributed Database Systems. Prentice Hall, second edition, 1999.
[17]
A. Pandit and H. Gupta. Communication-Efficient Implementation of Range-Joins in Sensor Networks. In Proceedings of the 11th International Conference on Database Systems for Advanced Applications (DASFAA '06), pages 859--869, Apr. 2006.
[18]
http://www.sensicast.com/.
[19]
http://sensorscope.epfl.ch/index.php/Environmental_Data.
[20]
M. Stern, E. Buchmann, and K. Böhm. Towards Efficient Processing of General-Purpose Joins in Sensor Networks. In Proceedings of the 2009 IEEE International Conference on Data Engineering (ICDE '09), pages 126--137, Mar. 2009.
[21]
A. Woo, T. Tong, and D. Culler. Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys '03), pages 14--27, Nov. 2003.
[22]
X. Yang, H. B. Lim, T. M. Özsu, and K. L. Tan. In-Network Execution of Monitoring Queries in Sensor Networks. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (SIGMOD '07), pages 521--532, June 2007.
[23]
Y. Yao and J. Gehrke. Query Processing for Sensor Networks. In Proceedings of the First Biennial Conference on Innovative Data Systems Research (CIDR '03), Jan. 2003.
[24]
M. L. Yiu, N. Mamoulis, and S. Bakiras. Retrieval of Spatial Join Pattern Instances from Sensor Networks. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management (SSDBM '07), page 25, July 2007.
[25]
H. Yu, E.-P. Lim, and J. Zhang. On In--network Synopsis Join Processing for Sensor Networks. In Proceedings of the 7th International Conference on Mobile Data Management (MDM '06), page 32, May 2006.
[26]
X. Zhu, H. Gupta, and B. Tang. Join of Multiple Data Streams in Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 99(1), Jan. 2009.

Cited By

View all

Index Terms

  1. Processing continuous join queries in sensor networks: a filtering approach

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
      June 2010
      1286 pages
      ISBN:9781450300322
      DOI:10.1145/1807167
      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: 06 June 2010

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. join processing
      2. sensor networks

      Qualifiers

      • Research-article

      Conference

      SIGMOD/PODS '10
      Sponsor:
      SIGMOD/PODS '10: International Conference on Management of Data
      June 6 - 10, 2010
      Indiana, Indianapolis, USA

      Acceptance Rates

      Overall Acceptance Rate 785 of 4,003 submissions, 20%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)6
      • Downloads (Last 6 weeks)3
      Reflects downloads up to 27 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2018)Query Optimization in Sensor NetworksEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_294(3015-3019)Online publication date: 7-Dec-2018
      • (2017)SAPIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.266223629:6(1310-1328)Online publication date: 1-Jun-2017
      • (2017)Data Reduction in Sensor Networks: Performance Evaluation in a Real EnvironmentIEEE Embedded Systems Letters10.1109/LES.2017.27493339:4(101-104)Online publication date: Dec-2017
      • (2016)Efficient In-Network Processing for a Hardware-Heterogeneous IoTProceedings of the 6th International Conference on the Internet of Things10.1145/2991561.2991568(93-101)Online publication date: 7-Nov-2016
      • (2016)Identifying defective nodes in wireless sensor networksDistributed and Parallel Databases10.1007/s10619-015-7189-734:4(591-610)Online publication date: 1-Dec-2016
      • (2015)In-Network Processing of an Iceberg Join Query in Wireless Sensor Networks Based on 2-Way Fragment SemijoinsSensors10.3390/s15030610515:3(6105-6132)Online publication date: 12-Mar-2015
      • (2015)Optimal processing node discovery algorithm for distributed computing in IoT2015 5th International Conference on the Internet of Things (IOT)10.1109/IOT.2015.7356550(72-79)Online publication date: Oct-2015
      • (2014)Multi-Attribute Join Query Processing in Sensor NetworksJournal of Networks10.4304/jnw.9.10.2702-27129:10Online publication date: 6-Oct-2014
      • (2014)Histogram Estimation for Optimal Filter Skyline Query Processing in Wireless Sensor NetworksInternational Journal of Distributed Sensor Networks10.1155/2014/68136810:7(681368)Online publication date: 15-Jul-2014
      • (2014)TWINS: Efficient time-windowed in-network joins for sensor networksInformation Sciences10.1016/j.ins.2013.09.026263(87-109)Online publication date: Apr-2014
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media