Medians and beyond: New aggregation techniques for sensor networks

Nisheeth Shrivastava, Chiranjeeb Buragohain, Divyakant Agrawal, Subhash Suri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

272 Citations (Scopus)

Abstract

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.

Original languageEnglish
Title of host publicationSenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems
Pages239-249
Number of pages11
Publication statusPublished - 1 Dec 2004
Externally publishedYes
EventSenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems - Baltimore, MD, United States
Duration: 3 Nov 20045 Nov 2004

Other

OtherSenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems
CountryUnited States
CityBaltimore, MD
Period3/11/045/11/04

Fingerprint

Sensor networks
Agglomeration
Sensors
Communication
Scalability
Wireless sensor networks
Information systems
Costs

Keywords

  • Aggregation
  • Approximation Algorithms
  • Distributed Algorithms
  • Sensor Networks

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Shrivastava, N., Buragohain, C., Agrawal, D., & Suri, S. (2004). Medians and beyond: New aggregation techniques for sensor networks. In SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems (pp. 239-249)

Medians and beyond : New aggregation techniques for sensor networks. / Shrivastava, Nisheeth; Buragohain, Chiranjeeb; Agrawal, Divyakant; Suri, Subhash.

SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems. 2004. p. 239-249.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shrivastava, N, Buragohain, C, Agrawal, D & Suri, S 2004, Medians and beyond: New aggregation techniques for sensor networks. in SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems. pp. 239-249, SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems, Baltimore, MD, United States, 3/11/04.
Shrivastava N, Buragohain C, Agrawal D, Suri S. Medians and beyond: New aggregation techniques for sensor networks. In SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems. 2004. p. 239-249
Shrivastava, Nisheeth ; Buragohain, Chiranjeeb ; Agrawal, Divyakant ; Suri, Subhash. / Medians and beyond : New aggregation techniques for sensor networks. SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems. 2004. pp. 239-249
@inproceedings{81e94b5a31894962a8dd598541242b6c,
title = "Medians and beyond: New aggregation techniques for sensor networks",
abstract = "Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.",
keywords = "Aggregation, Approximation Algorithms, Distributed Algorithms, Sensor Networks",
author = "Nisheeth Shrivastava and Chiranjeeb Buragohain and Divyakant Agrawal and Subhash Suri",
year = "2004",
month = "12",
day = "1",
language = "English",
isbn = "1581138792",
pages = "239--249",
booktitle = "SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems",

}

TY - GEN

T1 - Medians and beyond

T2 - New aggregation techniques for sensor networks

AU - Shrivastava, Nisheeth

AU - Buragohain, Chiranjeeb

AU - Agrawal, Divyakant

AU - Suri, Subhash

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.

AB - Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.

KW - Aggregation

KW - Approximation Algorithms

KW - Distributed Algorithms

KW - Sensor Networks

UR - http://www.scopus.com/inward/record.url?scp=27644511275&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27644511275&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:27644511275

SN - 1581138792

SN - 9781581138795

SP - 239

EP - 249

BT - SenSys'04 - Proceedings of the Second International Conference on Embedded Networked Sensor Systems

ER -