Faulty data detection in wireless sensor networks based on copula theory

Farid Lalem; Ahcène Bounceur; Rahim Kacimi; Reinhardt Euler; Massinissa Saoudi

doi:10.1145/3010089.3010114

Faulty data detection in wireless sensor networks based on copula theory

Farid Lalem
, Ahcène Bounceur
, Rahim Kacimi
, Reinhardt Euler
, Massinissa Saoudi

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

Abstract

Wireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the processing and the transmitting of this data over the network may drain the amount of available resources of WSNs and decrease rapidly the network lifetime. Therefore, there is an urgent need to detect faulty data in order to insure the reliability of data and keep the resource of WSNs at a high level. In this paper, we propose a new approach for faulty data detection in WSNs based on Copula theory. Our experimental results on real data sets collected by real sensor networks show that a significant percentage of the data are faulty1.

Original language	English
Title of host publication	Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016
Editors	Djallel Eddine Boubiche, Hani Hamdan, Ahcene Bounceur
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450347792
DOIs	https://doi.org/10.1145/3010089.3010114
State	Published - 10 Nov 2016
Externally published	Yes
Event	2016 International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016 - Blagoevgrad, Bulgaria Duration: 10 Nov 2016 → 11 Nov 2016

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2016 International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016
Country/Territory	Bulgaria
City	Blagoevgrad
Period	10/11/16 → 11/11/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

Copulas
Faulty Data
Outliers
Wireless Sensor Network

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3010089.3010114

Cite this

Lalem, F., Bounceur, A., Kacimi, R., Euler, R., & Saoudi, M. (2016). Faulty data detection in wireless sensor networks based on copula theory. In D. E. Boubiche, H. Hamdan, & A. Bounceur (Eds.), Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016 Article a29 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3010089.3010114

Lalem, Farid ; Bounceur, Ahcène ; Kacimi, Rahim et al. / Faulty data detection in wireless sensor networks based on copula theory. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016. editor / Djallel Eddine Boubiche ; Hani Hamdan ; Ahcene Bounceur. Association for Computing Machinery, 2016. (ACM International Conference Proceeding Series).

@inproceedings{12470c14a548463f99ccf250487cb65b,

title = "Faulty data detection in wireless sensor networks based on copula theory",

abstract = "Wireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the processing and the transmitting of this data over the network may drain the amount of available resources of WSNs and decrease rapidly the network lifetime. Therefore, there is an urgent need to detect faulty data in order to insure the reliability of data and keep the resource of WSNs at a high level. In this paper, we propose a new approach for faulty data detection in WSNs based on Copula theory. Our experimental results on real data sets collected by real sensor networks show that a significant percentage of the data are faulty1.",

keywords = "Copulas, Faulty Data, Outliers, Wireless Sensor Network",

author = "Farid Lalem and Ahc{\`e}ne Bounceur and Rahim Kacimi and Reinhardt Euler and Massinissa Saoudi",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016 ; Conference date: 10-11-2016 Through 11-11-2016",

year = "2016",

month = nov,

day = "10",

doi = "10.1145/3010089.3010114",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

editor = "Boubiche, \{Djallel Eddine\} and Hani Hamdan and Ahcene Bounceur",

booktitle = "Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016",

}

Lalem, F, Bounceur, A, Kacimi, R, Euler, R & Saoudi, M 2016, Faulty data detection in wireless sensor networks based on copula theory. in DE Boubiche, H Hamdan & A Bounceur (eds), Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016., a29, ACM International Conference Proceeding Series, Association for Computing Machinery, 2016 International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016, Blagoevgrad, Bulgaria, 10/11/16. https://doi.org/10.1145/3010089.3010114

Faulty data detection in wireless sensor networks based on copula theory. / Lalem, Farid; Bounceur, Ahcène; Kacimi, Rahim et al.
Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016. ed. / Djallel Eddine Boubiche; Hani Hamdan; Ahcene Bounceur. Association for Computing Machinery, 2016. a29 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Faulty data detection in wireless sensor networks based on copula theory

AU - Lalem, Farid

AU - Bounceur, Ahcène

AU - Kacimi, Rahim

AU - Euler, Reinhardt

AU - Saoudi, Massinissa

PY - 2016/11/10

Y1 - 2016/11/10

N2 - Wireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the processing and the transmitting of this data over the network may drain the amount of available resources of WSNs and decrease rapidly the network lifetime. Therefore, there is an urgent need to detect faulty data in order to insure the reliability of data and keep the resource of WSNs at a high level. In this paper, we propose a new approach for faulty data detection in WSNs based on Copula theory. Our experimental results on real data sets collected by real sensor networks show that a significant percentage of the data are faulty1.

AB - Wireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the processing and the transmitting of this data over the network may drain the amount of available resources of WSNs and decrease rapidly the network lifetime. Therefore, there is an urgent need to detect faulty data in order to insure the reliability of data and keep the resource of WSNs at a high level. In this paper, we propose a new approach for faulty data detection in WSNs based on Copula theory. Our experimental results on real data sets collected by real sensor networks show that a significant percentage of the data are faulty1.

KW - Copulas

KW - Faulty Data

KW - Outliers

KW - Wireless Sensor Network

UR - https://www.scopus.com/pages/publications/85015070338

U2 - 10.1145/3010089.3010114

DO - 10.1145/3010089.3010114

M3 - Conference contribution

AN - SCOPUS:85015070338

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016

A2 - Boubiche, Djallel Eddine

A2 - Hamdan, Hani

A2 - Bounceur, Ahcene

PB - Association for Computing Machinery

T2 - 2016 International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016

Y2 - 10 November 2016 through 11 November 2016

ER -

Lalem F, Bounceur A, Kacimi R, Euler R, Saoudi M. Faulty data detection in wireless sensor networks based on copula theory. In Boubiche DE, Hamdan H, Bounceur A, editors, Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, BDAW 2016. Association for Computing Machinery. 2016. a29. (ACM International Conference Proceeding Series). doi: 10.1145/3010089.3010114

Faulty data detection in wireless sensor networks based on copula theory

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this