Article

Energy-efficient monitoring of extreme values in sensor networks

Authors:

Adam Silberstein,

Kamesh Munagala,

Jun YangAuthors Info & Claims

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

Pages 169 - 180

https://doi.org/10.1145/1142473.1142493

Published: 27 June 2006 Publication History

Abstract

Monitoring extreme values (MAX or MIN) is a fundamental problem in wireless sensor networks (and in general, complex dynamic systems). This problem presents very different algorithmic challenges from aggregate and selection queries, in the sense that an individual node cannot by itself determine its inclusion in the query result. We present novel query processing algorithms for this problem, with the goal of minimizing message traffic in the network. These algorithms employ a hierarchy of local constraints, or thresholds, to leverage network topology such that message-passing is localized. We evaluate all algorithms using simulated and real-world data to study various trade-offs.

References

[1]

B. Babcock, M. Datar, R. Motwani, and L. O'Callaghan. Maintaining Variance and k-Medians over Data Stream Windows. In Proc. of the 2003 ACM Symp. on Principles of Database Systems, San Diego, California, USA, June 2003.

Digital Library

[2]

R. Cheng, B. Kao, S. Prabhakar, A. Kwan, and Y. Tu. Adaptive Stream Filters for Entity-based Queries with Non-Value Tolerance. In Proc. of the 2005 Intl. Conf. on Very Large Data Bases, Trondheim, Norway, Aug. 2005.

Digital Library

[3]

Chuck Conner. Modeling Heat Transfer in Parallel. http://www.cas.usf.edu/~cconnor/parallel/2dheat/2dheat.html.

[4]

J. Considine, F. Li, G. Kollios, and J. Byers. Approximate Aggregation Techniques for Sensor Databases. In Proc. of the 2004 Intl. Conf. on Data Engineering, Boston, Massachusetts, USA, Mar. 2004.

Digital Library

[5]

Crossbow Inc. MPR-Mote Processor Radio Board User's Manual.

[6]

A. Deligannakis, Y. Kotidis, and N. Roussopoulos. Hierarchical In-Network Data Aggregation with Quality Guarantees. In Proc. of the 2004 Intl. Conf. on Extending Database Technology, Heraklion, Crete, Mar. 2004.

[7]

Intel Berkeley Research Lab. http://berkeley.intel-research.net/labdata/.

[8]

Z. Liu, K. Sia, and J. Cho. Cost-Efficient Processing of Min/Max Queries over Distributed Sensors with Uncertainty. In Proc. of the 2004 ACM Symp. on Applied Computing, Santa Fe, New Mexico, USA, Mar. 2005.

Digital Library

[9]

S. Madden, M. Franklin, J. Hellerstein, and W. Hong. TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks. In Proc. of the 2002 USENIX Symp. on Operating Systems Design and Implementation, Boston, Massachusetts, USA, Dec. 2002.

Digital Library

[10]

S. Madden, R. Szewczyk, M. Franklin, and D. Culler. Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks. In Proc. of the 2002 IEEE Workshop on Mobile Computing Systems and Applications, Callicoon, New York, USA, June 2002.

Digital Library

[11]

C. Olston, B. Loo, and J. Widom. Adaptive Precision Setting for Cached Approximate Values. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, Santa Barbara, California, USA, May 2001.

Digital Library

[12]

S. Pattem, B. Krishnamachari, and R. Govindan. The Impact of Spatial Correlation on Routing with Compression in Wireless Sensor Networks. In Proc. of the 2004 Intl. Conf. on Information Processing in Sensor Networks, Berkeley, California, USA, Apr. 2004.

Digital Library

[13]

D. Petrovic, R. Shah, K. Ramchandran, and J. Rabaey. Data Funneling: Routing with Aggregation and Compression for Wireless Sensor Networks. In Proc. of the 2003 IEEE Sensor Network Protocols and Applications, Anchorage, Alaska, USA, May 2003.

[14]

N. Shrivastava, C. Buragohain, D. Agrawal, and S. Suri. Medians and Beyond: New Aggregation Techniques for Sensor Networks. In Proc. of the 2004 ACM Conf. on Embedded Networked Sensor Systems, Baltimore, Maryland, USA, Nov. 2004.

Digital Library

[15]

A. Silberstein, R. Braynard, C. Ellis, K. Munagala, and J. Yang. A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks. In Proc. of the 2006 Intl. Conf. on Data Engineering, Atlanta, Georgia, USA, Apr. 2006.

Digital Library

Cited By

Du HWang YXu XLiu M(2023)Niffler: Real-time Device-level Anomalies Detection in Smart HomeACM Transactions on the Web10.1145/358607317:3(1-27)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3586073
Yang JMunagala KSilberstein A(2018)Data Aggregation in Sensor NetworksEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_93(725-730)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_93
HARA T(2017)Data management issues in mobile ad hoc networksProceedings of the Japan Academy, Series B10.2183/pjab.93.01893:5(270-296)Online publication date: 2017
https://doi.org/10.2183/pjab.93.018
Show More Cited By

Index Terms

Energy-efficient monitoring of extreme values in sensor networks
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

June 2006

830 pages

ISBN:1595934340

DOI:10.1145/1142473

General Chairs:
Clement Yu
University of Illinois at Chicago
,
Peter Scheuermann
Northwestern University
,
Program Chair:
Surajit Chaudhuri
Microsoft Research

Copyright © 2006 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]

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Publication History

Published: 27 June 2006

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SIGMOD/PODS06

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SIGMOD/PODS06: International Conference on Management of Data and Symposium on Principles Database and Systems

June 27 - 29, 2006

IL, Chicago, USA

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

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Cited By

Du HWang YXu XLiu M(2023)Niffler: Real-time Device-level Anomalies Detection in Smart HomeACM Transactions on the Web10.1145/358607317:3(1-27)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3586073
Yang JMunagala KSilberstein A(2018)Data Aggregation in Sensor NetworksEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_93(725-730)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_93
HARA T(2017)Data management issues in mobile ad hoc networksProceedings of the Japan Academy, Series B10.2183/pjab.93.01893:5(270-296)Online publication date: 2017
https://doi.org/10.2183/pjab.93.018
Yang JMunagala KSilberstein A(2017)Data Aggregation in Sensor NetworksEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_93-2(1-6)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-1-4899-7993-3_93-2
Min J(2015)Efficient reverse skyline processing over sliding windows in wireless sensor networksInternational Journal of Distributed Sensor Networks10.1155/2015/3756302015(42-42)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/375630
(2015)Aggregate query processing in the presence of duplicates in wireless sensor networksInformation Sciences: an International Journal10.1016/j.ins.2014.11.021297:C(1-20)Online publication date: 10-Mar-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.11.021
Pan LGao HGao HLiu Y(2014)A Spatial Correlation Based Adaptive Missing Data Estimation Algorithm in Wireless Sensor NetworksInternational Journal of Wireless Information Networks10.1007/s10776-014-0253-921:4(280-289)Online publication date: 10-Oct-2014
https://doi.org/10.1007/s10776-014-0253-9
Min J(2013)CMOS: Efficient Clustered Data Monitoring in Sensor NetworksThe Scientific World Journal10.1155/2013/7049572013:1Online publication date: 25-Dec-2013
https://doi.org/10.1155/2013/704957
Zhang YCheng R(2013)Probabilistic filtersInformation Systems10.1016/j.is.2012.06.00338:1(132-154)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.is.2012.06.003
Amini RGaydadjiev GGill E(2012)Smart Power Management for an Onboard Wireless Sensors and Actuators NetworkAIAA SPACE 2009 Conference & Exposition10.2514/6.2009-6815Online publication date: 14-Jun-2012
https://doi.org/10.2514/6.2009-6815
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