article

TAG: a Tiny AGgregation service for ad-hoc sensor networks

Authors:

Michael J. Franklin,

Joseph M. Hellerstein,

Wei HongAuthors Info & Claims

ACM SIGOPS Operating Systems Review, Volume 36, Issue SI

Pages 131 - 146

https://doi.org/10.1145/844128.844142

Published: 31 December 2002 Publication History

Abstract

We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of optimizations for improving the performance and fault tolerance of the basic solution.

References

[1]

W. Adjue-Winoto, E. Schwartz, H. Balakrishnan, and J. Lilley. The design and implementation of an intentional naming system. In ACM SOSP, December 1999.]]

Digital Library

[2]

F. Bancilhon, T. Briggs, S. Khoshafian, and P. Valduriez. FAD, a powerful and simple database language. In VLDB, 1987.]]

Digital Library

[3]

D. Barbarfá, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. E. Ioannidis, H. Jagadish, T. Johnson, R. T. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey data reduction report. Data Engineering Bulletin, 20(4):3--45, 1997.]]

[4]

K. Calvert, J. Griffioen, and S. Wen. Lightweight network support for scalable end-to-end services. In ACM SIGCOMM, 2002.]]

Digital Library

[5]

A. Cerpa, J. Elson, D. Estrin, L. Girod, M. Hamilton, and J. Zhao. Habitat monitoring: Application driver for wireless communications technology. In ACM SIGCOMM Workshop on Data Communications in Latin America and the Caribbean, 2001.]]

Digital Library

[6]

S. Floyd, V. Jacobson, C. Liu, S. McCanne, and L. Zhang. A reliable multicase framework for light-weight sessions and application level framing. IEEE Transactions on Networking, 5(6):784--803, 1997.]]

Digital Library

[7]

D. Ganesan. Network dynamics in rene motes. PowerPoint Presentation, January 2002.]]

[8]

T. Goff, N. Abu-Ghazaleh, D. Phatak, and R. Kahvecioglu. Preemptive routing in ad hoc networks. In ACM MobiCom, July 2001.]]

Digital Library

[9]

J. Gray, A. Bosworth, A. Layman, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-total. In ICDE, February 1996.]]

Digital Library

[10]

J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan. Building efficient wireless sensor networks with low-level naming. In SOSP, October 2001.]]

Digital Library

[11]

J. Hellerstein, P. Hass, and H. Wang. Online aggregation. In Proceedings of the ACM SIGMOD, pages 171--182, Tucson, AZ, May 1997.]]

Digital Library

[12]

C. Intanagonwiwat, D. Estrin, R. Govindan, and J. Heidemann. Impact of network density on data aggregation in wireless sensor networks. Submitted for Publication, ICDCS-22, November 2001.]]

[13]

C. lntanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCOM, Boston, MA, August 2000.]]

Digital Library

[14]

J. Kulik, W. Rabiner, and H. Balakrishnan. Adaptive protocols for information dissemination in wireless sensor networks. In MobiCOM, 1999.]]

[15]

P.-Å. Larson. Data reduction by partial preaggregation. In ICDE, 2002.]]

[16]

P. Levis and D. Culler. Mat&eacute: A tiny virtual machine for sensor networks. Submitted for Publication.]]

[17]

J. Lin and S. Paul. RMTP: A Reliable Multicast Transport Protocol. In INFOCOM, pages 1414--1424, 1996.]]

Digital Library

[18]

S. Madden and M. J. Franklin. Fjording the stream: An architechture for queries over streaming sensor data. In ICDE, 2002.]]

[19]

S. Madden, W. Hong, J. Hellerstein, and M. Franklin. TinyDB web page. http://telegraph.cs.berkeley.edu/tinydb.]]

[20]

S. Madden, R. Szewczyk, M. Franklin, and D. Culler. Supporting aggregate queries over ad-hoc wireless sensor networks. In Workshop on Mobile Computing and Systems Applications, 2002.]]

Digital Library

[21]

A. Mainwaring, J. Polastre, R. Szewczyk, and D. Culler. Wireless sensor networks for habitat monitoring. In ACM Workshop on Sensor Networks and Applications, 2002.]]

Digital Library

[22]

V. D. Park and M. S. Corson. A highly adaptive distributed routing algorithm for mobile wireless networks. In INFOCOM, 1997.]]

Digital Library

[23]

P. Bonnet, J. Gehrke, and P. Seshadri. Towards sensor database systems. In Conference on Mobile Data Management, January 2001.]]

Digital Library

[24]

C. E. Perkins and E. M. Royer. Ad-hoc on-demand distance vector routing. In Workshop on Mobile Computing and Systems Applications, 1999.]]

Digital Library

[25]

G. Pottie and W. Kaiser. Wireless integrated network sensors. Communications of the ACM, 43(5):51--58, May 2000.]]

Digital Library

[26]

A. Shatdal and J. Naughton. Adaptive parallel aggregation algorithms. In ACM SIGMOD, 1995.]]

Digital Library

[27]

R. T. Snodgrass, editor. The TSQL2 Temporal Query Language. Kluwer Academic Publisher, 1995.]]

Digital Library

[28]

L. Subramanian and R. H.Katz. An architecture for building self-configurable systems. In MobiHOC, Boston, August 2000.]]

Digital Library

[29]

K.-L. Tan, C. H. Goh, and B. C. Ooi. Online feedback for nested aggregate queries with multi-threading. In VLDB, 1999.]]

Digital Library

[30]

D. L. Tennenhouse, J. M. Smith, W. D. Sincoskie, D. J. Wetherall, and G. J. Minden. A survery of active network research. IEEE Communications, 1997.]]

[31]

UC Berkeley. Smart buildings admit their faults. Web Page, November 2001. Lab Notes: Research from the College of Engineering, UC Berkeley. http://coe.berkeley.edu/labnotes/1101.smartbuildings.html.]]

[32]

A. Woo and D. Culler. A transmission control scheme for media access in sensor networks. In ACM Mobicom, July 2001.]]

Digital Library

[33]

W. P. Yan and P. Å. Larson. Eager aggregation and lazy aggregation. In VLDB, 1995.]]

Digital Library

[34]

W. Ye, J. Heidemann, and D. Estrin. An energy-efficient MAC protocol for wireless sensor networks. In IEEE Infocom, 2002.]]

[35]

A. Yu and J. Chen. The POSTGRES95 User Manual. UC Berkeley, 1995.]]

Cited By

Sen J(2023)A Survey of Cryptography and Key Management Schemes for Wireless Sensor NetworksWireless Sensor Networks - Design, Applications and Challenges10.5772/intechopen.112277Online publication date: 18-Oct-2023
https://doi.org/10.5772/intechopen.112277
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
Sharan BManjula R(2023)Cluster-Head Selection Protocol for Improving the Network Lifetime of Wireless Sensor Network2023 9th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC60394.2023.10441568(72-77)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ICSC60394.2023.10441568
Show More Cited By

Index Terms

TAG: a Tiny AGgregation service for ad-hoc sensor networks

Recommendations

TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation (Copyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading)

We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless ...
EnviS Tag, Scan, View: A Location-Based App for Visualizing Spatio-temporal Data from Sensor Cloud
MDM '14: Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management - Volume 01

This paper presents a smartphone app connected to a sensor cloud for spatio-temporal management and 3D visualization of data from ad-hoc wireless sensor networks (WSN) and Internet of Things (IoT). Many existing sensor cloud services and sensor data ...
Wireless Sensor Tag and Network for Improved Clinical Triage
DSD '15: Proceedings of the 2015 Euromicro Conference on Digital System Design

This paper presents a solution to improve clinical triage with a network of Wireless Sensor Tags based on pulse oximeters. The wireless sensor measures vital signs on a patient and outputs an automated clinical analysis. A central unit monitors these ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGOPS Operating Systems Review

ACM SIGOPS Operating Systems Review Volume 36, Issue SI

OSDI '02: Proceedings of the 5th Symposium on Operating Systems Design and Implementation

Winter 2002

398 pages

ISSN:0163-5980

DOI:10.1145/844128

Issue’s Table of Contents

Copyright © 2002 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 December 2002

Published in SIGOPS Volume 36, Issue SI

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

582
Total Citations
View Citations
3,898
Total Downloads

Downloads (Last 12 months)75
Downloads (Last 6 weeks)8

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sen J(2023)A Survey of Cryptography and Key Management Schemes for Wireless Sensor NetworksWireless Sensor Networks - Design, Applications and Challenges10.5772/intechopen.112277Online publication date: 18-Oct-2023
https://doi.org/10.5772/intechopen.112277
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
Sharan BManjula R(2023)Cluster-Head Selection Protocol for Improving the Network Lifetime of Wireless Sensor Network2023 9th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC60394.2023.10441568(72-77)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ICSC60394.2023.10441568
Gotfryd KCichoń J(2023)On distributed data aggregation and the precision of approximate histogramsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104722180:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.jpdc.2023.104722
Begum BNandury S(2023)Data aggregation protocols for WSN and IoT applications – A comprehensive surveyJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.01.00835:2(651-681)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.01.008
Fahmy HFahmy H(2023)Cross-Layer Protocols for WSNsConcepts, Applications, Experimentation and Analysis of Wireless Sensor Networks10.1007/978-3-031-20709-9_6(345-412)Online publication date: 14-Feb-2023
https://doi.org/10.1007/978-3-031-20709-9_6
Dash LPattanayak BMishra SSahoo KJhanjhi NBaz MMasud M(2022)A Data Aggregation Approach Exploiting Spatial and Temporal Correlation among Sensor Data in Wireless Sensor NetworksElectronics10.3390/electronics1107098911:7(989)Online publication date: 23-Mar-2022
https://doi.org/10.3390/electronics11070989
Rahmani MKhan FMuzaffar AJan M(2022)Internet of Things-Enabled Optimal Data Aggregation Approach for the Intelligent Surveillance SystemsMobile Information Systems10.1155/2022/46815832022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4681583
Gu FHu X(2022)Research on Efficient Top-k Query Based on ARIMA Time Series ModelWireless Communications & Mobile Computing10.1155/2022/45106252022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4510625
Feng LLiu B(2022)Low-Energy Data Fusion Privacy Protection Algorithm for Three-Dimensional Wireless Sensor NetworkMobile Information Systems10.1155/2022/35806072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3580607
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 Issue’s Table of Contents