Prediction based mobile data aggregation in wireless sensor network

Sang Bin Lee, Songmin Kim, Doohyun Ko, Sungjun Kim, Sun-Shin An

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

6 Citations (Scopus)

Abstract

A wireless sensor network consists of many energyautonomous micro-sensors distributed throughout an area of interest. Each node has a limited energy supply and generates information that needs to be communicated to a sink node. To reduce costs, the data sent via intermediate sensors to a sink, are often aggregated. The existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, the centralized and distributed approaches, each having its unique strengths and weaknesses. In this paper, we introduce PMDA (Prediction based Mobile Data Aggregation) scheme which uses a novel data aggregation scheme to utilize the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. This knowledge (geo-location and transmission range of the mobile node) is useful for gathering the data of the mobile node. Hence, the sensor nodes can construct a near-optimal aggregation tree by itself, using the knowledge of the mobile node, which is a similar process to forming the centralized aggregation tree. We show that the PMDA is a near-optimal data aggregation scheme with mobility support, achieving energy and delay efficiency. This data aggregation scheme is proven to outperform the other general data aggregation schemes by our experimental results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages328-339
Number of pages12
Volume5529
DOIs
Publication statusPublished - 2009 Jul 15
Event4th International Conference on Grid and Pervasive Computing, GPC 2009 - Geneva, Switzerland
Duration: 2009 May 42009 May 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5529
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Conference on Grid and Pervasive Computing, GPC 2009
CountrySwitzerland
CityGeneva
Period09/5/409/5/8

Fingerprint

Data Aggregation
Wireless Sensor Networks
Wireless sensor networks
Agglomeration
Prediction
Vertex of a graph
Aggregation
Sensor
Distributed Sensor
Sensors
Energy
Energy Efficient
Sensor nodes
Sensor networks
Sensor Networks
Infrastructure

Keywords

  • Data aggregation
  • Mobility
  • Prediction
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lee, S. B., Kim, S., Ko, D., Kim, S., & An, S-S. (2009). Prediction based mobile data aggregation in wireless sensor network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5529, pp. 328-339). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5529). https://doi.org/10.1007/978-3-642-01671-4_30

Prediction based mobile data aggregation in wireless sensor network. / Lee, Sang Bin; Kim, Songmin; Ko, Doohyun; Kim, Sungjun; An, Sun-Shin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5529 2009. p. 328-339 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5529).

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

Lee, SB, Kim, S, Ko, D, Kim, S & An, S-S 2009, Prediction based mobile data aggregation in wireless sensor network. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5529, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5529, pp. 328-339, 4th International Conference on Grid and Pervasive Computing, GPC 2009, Geneva, Switzerland, 09/5/4. https://doi.org/10.1007/978-3-642-01671-4_30
Lee SB, Kim S, Ko D, Kim S, An S-S. Prediction based mobile data aggregation in wireless sensor network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5529. 2009. p. 328-339. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-01671-4_30
Lee, Sang Bin ; Kim, Songmin ; Ko, Doohyun ; Kim, Sungjun ; An, Sun-Shin. / Prediction based mobile data aggregation in wireless sensor network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5529 2009. pp. 328-339 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{202beed45bd04d929760c38688da17b5,
title = "Prediction based mobile data aggregation in wireless sensor network",
abstract = "A wireless sensor network consists of many energyautonomous micro-sensors distributed throughout an area of interest. Each node has a limited energy supply and generates information that needs to be communicated to a sink node. To reduce costs, the data sent via intermediate sensors to a sink, are often aggregated. The existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, the centralized and distributed approaches, each having its unique strengths and weaknesses. In this paper, we introduce PMDA (Prediction based Mobile Data Aggregation) scheme which uses a novel data aggregation scheme to utilize the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. This knowledge (geo-location and transmission range of the mobile node) is useful for gathering the data of the mobile node. Hence, the sensor nodes can construct a near-optimal aggregation tree by itself, using the knowledge of the mobile node, which is a similar process to forming the centralized aggregation tree. We show that the PMDA is a near-optimal data aggregation scheme with mobility support, achieving energy and delay efficiency. This data aggregation scheme is proven to outperform the other general data aggregation schemes by our experimental results.",
keywords = "Data aggregation, Mobility, Prediction, Wireless sensor networks",
author = "Lee, {Sang Bin} and Songmin Kim and Doohyun Ko and Sungjun Kim and Sun-Shin An",
year = "2009",
month = "7",
day = "15",
doi = "10.1007/978-3-642-01671-4_30",
language = "English",
isbn = "9783642016707",
volume = "5529",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "328--339",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Prediction based mobile data aggregation in wireless sensor network

AU - Lee, Sang Bin

AU - Kim, Songmin

AU - Ko, Doohyun

AU - Kim, Sungjun

AU - An, Sun-Shin

PY - 2009/7/15

Y1 - 2009/7/15

N2 - A wireless sensor network consists of many energyautonomous micro-sensors distributed throughout an area of interest. Each node has a limited energy supply and generates information that needs to be communicated to a sink node. To reduce costs, the data sent via intermediate sensors to a sink, are often aggregated. The existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, the centralized and distributed approaches, each having its unique strengths and weaknesses. In this paper, we introduce PMDA (Prediction based Mobile Data Aggregation) scheme which uses a novel data aggregation scheme to utilize the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. This knowledge (geo-location and transmission range of the mobile node) is useful for gathering the data of the mobile node. Hence, the sensor nodes can construct a near-optimal aggregation tree by itself, using the knowledge of the mobile node, which is a similar process to forming the centralized aggregation tree. We show that the PMDA is a near-optimal data aggregation scheme with mobility support, achieving energy and delay efficiency. This data aggregation scheme is proven to outperform the other general data aggregation schemes by our experimental results.

AB - A wireless sensor network consists of many energyautonomous micro-sensors distributed throughout an area of interest. Each node has a limited energy supply and generates information that needs to be communicated to a sink node. To reduce costs, the data sent via intermediate sensors to a sink, are often aggregated. The existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, the centralized and distributed approaches, each having its unique strengths and weaknesses. In this paper, we introduce PMDA (Prediction based Mobile Data Aggregation) scheme which uses a novel data aggregation scheme to utilize the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. This knowledge (geo-location and transmission range of the mobile node) is useful for gathering the data of the mobile node. Hence, the sensor nodes can construct a near-optimal aggregation tree by itself, using the knowledge of the mobile node, which is a similar process to forming the centralized aggregation tree. We show that the PMDA is a near-optimal data aggregation scheme with mobility support, achieving energy and delay efficiency. This data aggregation scheme is proven to outperform the other general data aggregation schemes by our experimental results.

KW - Data aggregation

KW - Mobility

KW - Prediction

KW - Wireless sensor networks

UR - http://www.scopus.com/inward/record.url?scp=67650143841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67650143841&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-01671-4_30

DO - 10.1007/978-3-642-01671-4_30

M3 - Conference contribution

AN - SCOPUS:67650143841

SN - 9783642016707

VL - 5529

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 328

EP - 339

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -