Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

Massinissa Saoudi; Ahcene Bounceur; Reinhardt Euler; Tahar Kechadi; Alfredo Cuzzocrea

doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

Massinissa Saoudi
, Ahcene Bounceur
, Reinhardt Euler
, Tahar Kechadi
, Alfredo Cuzzocrea

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

13 Scopus citations

Abstract

Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.

Original language	English
Title of host publication	Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016
Editors	Didier El Baz, Julien Bourgeois
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	766-771
Number of pages	6
ISBN (Electronic)	9781509027705
DOIs	https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123
State	Published - 12 Jan 2017
Externally published	Yes
Event	13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016 - Toulouse, France Duration: 18 Jul 2016 → 21 Jul 2016

Publication series

Name

Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

Conference

Conference	13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016
Country/Territory	France
City	Toulouse
Period	18/07/16 → 21/07/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Data Mining
Fire detection
Intelligent Decision Making
Wireless sensor networks

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

Cite this

Saoudi, M., Bounceur, A., Euler, R., Kechadi, T., & Cuzzocrea, A. (2017). Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks. In D. El Baz, & J. Bourgeois (Eds.), Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016 (pp. 766-771). Article 7816919 (Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

Saoudi, Massinissa ; Bounceur, Ahcene ; Euler, Reinhardt et al. / Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks. Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016. editor / Didier El Baz ; Julien Bourgeois. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 766-771 (Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016).

@inproceedings{9be32f565e6b481a863e1d20045de1ff,

title = "Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks",

abstract = "Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.",

keywords = "Data Mining, Fire detection, Intelligent Decision Making, Wireless sensor networks",

author = "Massinissa Saoudi and Ahcene Bounceur and Reinhardt Euler and Tahar Kechadi and Alfredo Cuzzocrea",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016 ; Conference date: 18-07-2016 Through 21-07-2016",

year = "2017",

month = jan,

day = "12",

doi = "10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123",

language = "English",

series = "Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "766--771",

editor = "\{El Baz\}, Didier and Julien Bourgeois",

booktitle = "Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016",

address = "United States",

}

Saoudi, M, Bounceur, A, Euler, R, Kechadi, T & Cuzzocrea, A 2017, Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks. in D El Baz & J Bourgeois (eds), Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016., 7816919, Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016, Institute of Electrical and Electronics Engineers Inc., pp. 766-771, 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016, Toulouse, France, 18/07/16. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks. / Saoudi, Massinissa; Bounceur, Ahcene; Euler, Reinhardt et al.
Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016. ed. / Didier El Baz; Julien Bourgeois. Institute of Electrical and Electronics Engineers Inc., 2017. p. 766-771 7816919 (Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016).

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

TY - GEN

T1 - Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

AU - Saoudi, Massinissa

AU - Bounceur, Ahcene

AU - Euler, Reinhardt

AU - Kechadi, Tahar

AU - Cuzzocrea, Alfredo

PY - 2017/1/12

Y1 - 2017/1/12

N2 - Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.

AB - Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.

KW - Data Mining

KW - Fire detection

KW - Intelligent Decision Making

KW - Wireless sensor networks

UR - https://www.scopus.com/pages/publications/85013187950

U2 - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

DO - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

M3 - Conference contribution

AN - SCOPUS:85013187950

T3 - Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

SP - 766

EP - 771

BT - Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

A2 - El Baz, Didier

A2 - Bourgeois, Julien

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

Y2 - 18 July 2016 through 21 July 2016

ER -

Saoudi M, Bounceur A, Euler R, Kechadi T, Cuzzocrea A. Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks. In El Baz D, Bourgeois J, editors, Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 766-771. 7816919. (Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016). doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0123

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this