An efficient data extraction framework for mining wireless sensor networks

Md. Mamunur Rashid; Iqbal Gondal; Joarder Kamruzzaman

doi:10.1007/978-3-319-46675-0_54

An efficient data extraction framework for mining wireless sensor networks

Md. Mamunur Rashid, Iqbal Gondal, Joarder Kamruzzaman

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor’s activities over time from the sensor database that attached with a wellequipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associatedcorrelated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III
Editors	Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	491-498
Number of pages	8
ISBN (Electronic)	9783319466750
ISBN (Print)	9783319466743
DOIs	https://doi.org/10.1007/978-3-319-46675-0_54
Publication status	Published - 2016
Event	International Conference on Neural Information Processing 2016 - Kyoto, Japan Duration: 16 Oct 2016 → 21 Oct 2016 Conference number: 23rd https://link.springer.com/book/10.1007/978-3-319-46687-3 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9949
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2016
Abbreviated title	ICONIP 2016
Country/Territory	Japan
City	Kyoto
Period	16/10/16 → 21/10/16
Internet address	https://link.springer.com/book/10.1007/978-3-319-46687-3 (Proceedings)

Keywords

Associated-correlated sensor pattern
Data extraction
Data mining
Knowledge discovery
Wireless sensor networks

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10.1007/978-3-319-46675-0_54

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Rashid, M. M., Gondal, I., & Kamruzzaman, J. (2016). An efficient data extraction framework for mining wireless sensor networks. In A. Hirose, S. Ozawa, K. Doya, K. Ikeda, M. Lee, & D. Liu (Eds.), Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III (pp. 491-498). (Lecture Notes in Computer Science; Vol. 9949 ). Springer. https://doi.org/10.1007/978-3-319-46675-0_54

Rashid, Md. Mamunur ; Gondal, Iqbal ; Kamruzzaman, Joarder. / An efficient data extraction framework for mining wireless sensor networks. Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III. editor / Akira Hirose ; Seiichi Ozawa ; Kenji Doya ; Kazushi Ikeda ; Minho Lee ; Derong Liu. Cham, Switzerland : Springer, 2016. pp. 491-498 (Lecture Notes in Computer Science).

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title = "An efficient data extraction framework for mining wireless sensor networks",

abstract = "Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor{\textquoteright}s activities over time from the sensor database that attached with a wellequipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associatedcorrelated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.",

keywords = "Associated-correlated sensor pattern, Data extraction, Data mining, Knowledge discovery, Wireless sensor networks",

author = "Rashid, {Md. Mamunur} and Iqbal Gondal and Joarder Kamruzzaman",

year = "2016",

doi = "10.1007/978-3-319-46675-0_54",

language = "English",

isbn = "9783319466743",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "491--498",

editor = "Hirose, {Akira } and Ozawa, {Seiichi } and Doya, {Kenji } and Ikeda, {Kazushi } and Lee, {Minho } and Liu, {Derong }",

booktitle = "Neural Information Processing",

note = "International Conference on Neural Information Processing 2016, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",

url = "https://link.springer.com/book/10.1007/978-3-319-46687-3",

}

Rashid, MM, Gondal, I & Kamruzzaman, J 2016, An efficient data extraction framework for mining wireless sensor networks. in A Hirose, S Ozawa, K Doya, K Ikeda, M Lee & D Liu (eds), Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III. Lecture Notes in Computer Science, vol. 9949 , Springer, Cham, Switzerland, pp. 491-498, International Conference on Neural Information Processing 2016, Kyoto, Japan, 16/10/16. https://doi.org/10.1007/978-3-319-46675-0_54

An efficient data extraction framework for mining wireless sensor networks. / Rashid, Md. Mamunur; Gondal, Iqbal; Kamruzzaman, Joarder.

Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III. ed. / Akira Hirose; Seiichi Ozawa; Kenji Doya; Kazushi Ikeda; Minho Lee; Derong Liu. Cham, Switzerland : Springer, 2016. p. 491-498 (Lecture Notes in Computer Science; Vol. 9949 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Rashid, Md. Mamunur

AU - Gondal, Iqbal

AU - Kamruzzaman, Joarder

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N2 - Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor’s activities over time from the sensor database that attached with a wellequipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associatedcorrelated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

AB - Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor’s activities over time from the sensor database that attached with a wellequipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associatedcorrelated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

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Rashid MM, Gondal I, Kamruzzaman J. An efficient data extraction framework for mining wireless sensor networks. In Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D, editors, Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III. Cham, Switzerland: Springer. 2016. p. 491-498. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-46675-0_54

An efficient data extraction framework for mining wireless sensor networks

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Keywords

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