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Using fractal clustering to explore behavioral correlation: a new approach to reduce energy consumption in WSN

Authors:

Fernando Rodrigues,

Angelo Brayner,

Jose E. Bessa MaiaAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 589 - 591

https://doi.org/10.1145/2695664.2696007

Published: 13 April 2015 Publication History

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Abstract

Sensor clustering is an efficient strategy to reduce the number of messages flowing in a Wireless Sensor Network (WSN) and thereby reducing the energy consumption. This paper presents a new approach to cluster sensors in WSNs, called Behavioral Correlation in WSN (BCWSN), which is based on the behavior of recent historical data collected by sensors. The proposed approach initializes clusters using the concepts of similarity in magnitude and trend of sensed data, and implements the notion of Fractal Clustering to dynamically find the best configuration for clusters. BCWSN can reduce the number of messages injected into the network when compared to approaches implementing temporal correlation, while RMSE remains roughly stable.

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Cited By

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Lemos MFilho RRabêlo Rde Carvalho CMendes DCosta V(2018)An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds EnvironmentsSensors10.3390/s1803068918:3(689)Online publication date: 26-Feb-2018
https://doi.org/10.3390/s18030689
Almeida FBrayner ARodrigues JMaia J(2017)Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal ClusteringSensors10.3390/s1706131717:6(1317)Online publication date: 7-Jun-2017
https://doi.org/10.3390/s17061317
Zou TLin SLi S(2016)Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy ControlSensors10.3390/s1606078816:6(788)Online publication date: 31-May-2016
https://doi.org/10.3390/s16060788
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Using fractal clustering to explore behavioral correlation: a new approach to reduce energy consumption in WSN

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Published In

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 13 April 2015

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SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

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Lemos MFilho RRabêlo Rde Carvalho CMendes DCosta V(2018)An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds EnvironmentsSensors10.3390/s1803068918:3(689)Online publication date: 26-Feb-2018
https://doi.org/10.3390/s18030689
Almeida FBrayner ARodrigues JMaia J(2017)Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal ClusteringSensors10.3390/s1706131717:6(1317)Online publication date: 7-Jun-2017
https://doi.org/10.3390/s17061317
Zou TLin SLi S(2016)Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy ControlSensors10.3390/s1606078816:6(788)Online publication date: 31-May-2016
https://doi.org/10.3390/s16060788
Sivakumar BSowmya B(2016)An Energy Efficient Clustering with Delay Reduction in Data Gathering (EE-CDRDG) Using Mobile Sensor NodeWireless Personal Communications: An International Journal10.1007/s11277-016-3214-z90:2(793-806)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s11277-016-3214-z

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Recommendations

Adaptive decentralized re-clustering protocol for wireless sensor networks

An energy efficient clustering method for wireless sensor networks

Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach

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