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An Adaptive And Efficient Dimension Reduction Model For Multivariate Wireless Sensor Networks Applications

M. Rassam, A. Zainal, M. A. Maarof
Published 2013 · Computer Science, Mathematics

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Wireless sensor networks (WSNs) applications are growing rapidly in various fields such as environmental monitoring, health care management, and industry control. However, WSN's are characterized by constrained resources especially; energy which shortens their lifespan. One of the most important factors that cause a rapid drain of energy is radio communication of multivariate data between nodes and base station. Besides, the dynamic changes of environmental variables pose a need for an adaptive solution that cope with these changes over the time. In this paper, a new adaptive and efficient dimension reduction model (APCADR) is proposed for hierarchical sensor networks based on the candid covariance-free incremental PCA (CCIPCA). The performance of the model is evaluated using three real sensor networks datasets collected at Intel Berkeley Research Lab (IBRL), Great St. Bernard (GSB) area, and Lausanne Urban Canopy Experiments (LUCE). Experimental results show 33.33% and 50% reduction of multivariate data in dynamic and static environments, respectively. Results also show that 97-99% of original data is successfully approximated at cluster heads in both environment types. A comparison with the multivariate linear regression model (MLR) and simple linear regression model (SLR) shows the advantage of the proposed model in terms of efficiency, approximation accuracy, and adaptability with dynamic environmental changes.
This paper references
Unsupervised and supervised compression with principal component analysis in wireless sensor networks
Y. Borgne (2007)
10.1016/0165-1684(84)90028-8
Subspace methods of pattern recognition
E. Oja (1983)
10.1109/SAHCN.2008.54
Adaptive Radio Modes in Sensor Networks: How Deep to Sleep?
R. Jurdak (2008)
10.1109/ISSNIP.2008.4761978
An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine
Z. Yang (2008)
Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition
Ghazy M. R. Assassa (2009)
10.1145/356989.356998
System Architecture Directions for Networked Sensors
J. Hill (2000)
10.1007/11596042_21
Multivariate Stream Data Reduction in Sensor Network Applications
S. Seo (2005)
10.1145/1993042.1993045
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns
Ethan W. Dereszynski (2011)
10.3390/s8084821
Distributed Principal Component Analysis for Wireless Sensor Networks
Y. Borgne (2008)
10.1109/IPSN.2005.1440950
Telos: enabling ultra-low power wireless research
J. Polastre (2005)
10.1109/WAINA.2009.200
Adaptive and Online One-Class Support Vector Machine-Based Outlier Detection Techniques for Wireless Sensor Networks
Y. Zhang (2009)
10.1016/j.comnet.2011.09.016
CCIPCA-OPCSC: An online method for detecting shared congestion paths
Huali Bai (2012)
10.1145/1774088.1774210
Towards in-network data prediction in wireless sensor networks
Tales Benigno Matos (2010)
10.1016/j.patcog.2010.07.024
Clustering ellipses for anomaly detection
Masud Moshtaghi (2011)
10.1016/S1389-1286(01)00302-4
Wireless sensor networks: a survey
I. Akyildiz (2002)
10.1109/ICDCS.2006.49
In-Network Outlier Detection in Wireless Sensor Networks
Joel W. Branch (2006)
10.1109/ICC.2009.56
Face Recognition Using Incremental Principal Components Analysis
H. Aboalsamh (2009)
10.1109/WICOM.2010.5601180
Algorithm of Data Compression Based on Multiple Principal Component Analysis over the WSN
Fenxiong Chen (2010)
10.1109/.2005.1467103
A Survey of Applications of Wireless Sensors and Wireless Sensor Networks
T. Arampatzis (2005)
10.1007/978-3-642-02903-5_4
Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks
Y. Zhang (2009)
10.1109/ISSNIP.2010.5706781
Reducing the data transmission in Wireless Sensor Networks using the Principal Component Analysis
Pedram Rooshenas (2010)
Bontempi , Unsupervised and supervised compression with principal component analysis in wireless sensor networks
G. Y. Le Borgne (2007)
10.1016/j.comnet.2008.04.002
Wireless sensor network survey
J. Yick (2008)
10.1109/TPAMI.2003.1217609
Candid Covariance-Free Incremental Principal Component Analysis
J. Weng (2003)
10.2172/15002155
A Survey of Dimension Reduction Techniques
I. Fodor (2002)
10.1016/j.comcom.2008.06.020
Group-based intrusion detection system in wireless sensor networks
G. Li (2008)
10.1109/TrustCom.2011.73
Highly Efficient Distance-Based Anomaly Detection through Univariate with PCA in Wireless Sensor Networks
Miao Xie (2011)
10.3990/1.9789036530583
Observing the unobservable : distributed online outlier detection in wireless sensor networks
Y. Zhang (2010)
10.1109/JSEN.2007.894147
Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks
V. Chatzigiannakis (2007)
10.1515/9783111576855-009
D
Saskia Bonjour (1824)
10.1109/ICPR.2002.1048133
Incremental PCA for on-line visual learning and recognition
M. Artac (2002)
10.1109/ICCS.2006.301508
Distributed Anomaly Detection in Wireless Sensor Networks
S. Rajasegarar (2006)
10.1145/1993042.1993043
Path Planning of Data Mules in Sensor Networks
Ryo Sugihara (2011)
10.1109/ICCCNT.2010.5591850
Outlier aware data aggregation in distributed wireless sensor network using robust principal component analysis
N. Chitradevi (2010)
Bontempi , Distributed principal component analysis for wireless sensor networks
S. Raybaud Y. A. Le Borgne (2008)
10.1002/0470013192.bsa501
Principal Component Analysis
I. T. Jolliffe (2005)
10.3390/s111110010
Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation
Carlos Giovanni Nunes de Carvalho (2011)
10.1109/ICC.2007.637
Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks
S. Rajasegarar (2007)
10.1109/ROBOT.2002.1013490
Mobile robot localization using an incremental eigenspace model
M. Artac (2002)
Bontempi , Unsupervised and supervised compression with principal component analysis in wireless sensor networks
G. Y. Le Borgne (2007)
10.1109/ISSNIP.2009.5416818
Anomaly detection by clustering ellipsoids in wireless sensor networks
Masud Moshtaghi (2009)
10.1109/TIFS.2010.2051543
Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks
S. Rajasegarar (2010)
10.1109/SENSORCOMM.2007.27
Analysis of Anomalies in IBRL Data from a Wireless Sensor Network Deployment
S. Rajasegarar (2007)
Ghosal , Wireless sensor network survey
B. Mukherjee J. Yick (2008)
Distributed PCA-based anomaly detection in wireless sensor networks
M. A. Livani (2010)
10.1109/ISCC.2009.5202248
Multivariate reduction in wireless sensor networks
Orlando Silva Junior (2009)
10.1016/0022-247X(85)90131-3
On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
E. Oja (1985)
10.1109/MWC.2004.1368893
Routing techniques in wireless sensor networks: a survey
Jamal N. Al-Karaki (2004)
10.1109/IJCNN.2009.5178997
Adaptive incremental principal component analysis in nonstationary online learning environments
Seiichi Ozawa (2009)
10.1137/1.9780898719581
Templates for the Solution of Algebraic Eigenvalue Problems
J. Demmel (2000)
10.1109/LANOMS.2011.6102268
Multiple linear regression to improve prediction accuracy in WSN data reduction
Carlos Giovanni Nunes de Carvalho (2011)
10.1109/CNDS.2011.5764585
A PCA-based distributed approach for intrusion detection in wireless sensor networks
M. A. Livani (2011)
10.1109/AIMSEC.2011.6009921
Data fault detection for wireless sensor networks using multi-scale PCA method
Ying-xin Xie (2011)



This paper is referenced by
A Review of Various Protocols for WSNs in Search of Most Efficient Congestion Control and Clustering Techniques
Prabhdeep Kaur (2016)
10.1109/SOFTCOM.2016.7772168
One class outlier detection method in wireless sensor networks: Comparative study
Oussama Ghorbel (2016)
10.1051/MATECCONF/20167602039
Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network
Abeer Hamdy (2016)
10.1007/978-981-15-0637-6_41
Outlier Detection Method-Based KPCA for Water Pipeline in Wireless Sensor Networks
Mohammed Aseeri (2019)
10.5815/IJMECS.2019.04.02
High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study
Hussein Hassan Shia (2019)
10.1155/2015/521784
Network Structure-Aware Ant-Based Routing in Large-Scale Wireless Sensor Networks
K. Kim (2015)
Analysis of Sensor Nodes with Distinct Angles using Scalable Network
Swati Singh (2014)
10.2174/1872212112666181116125033
Supervised Dimension Reduction by Local Neighborhood Optimization for Image Processing
Liyan Zhao (2019)
Augmenting the Network Lifetime through Enhanced LEACH
Swati Singh (2014)
10.1002/cpe.3338
Dynamic malicious node detection with semi‐supervised multivariate classification in cognitive wireless sensor networks
Hongjun Dai (2015)
10.1155/2015/146189
A Novel Distributed Online Anomaly Detection Method in Resource-Constrained Wireless Sensor Networks
Zhiguo Ding (2015)
10.5120/13092-0373
Improving the Network Lifetime in WSN through Enhanced LEACH
R. Garg (2013)
Conference on Parallel and Distributed Computing and Systems An Efficient Distributed Anomaly Detection Model for Wireless Sensor Networks
Murad A. Rassam (2013)
Performance Analysis of Improved Congestion Control Clustering Protocol Using TBA/WDM
D. Kumar (2015)
10.3390/s20041011
Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks
Muhammad Kashif Alam (2020)
10.1016/j.asoc.2014.11.061
Multisignal 1-D compression by F-transform for wireless sensor networks applications
Matteo Gaeta (2015)
10.1109/LSENS.2017.2768218
Performance Evaluation of Real-Time Multivariate Data Reduction Models for Adaptive-Threshold in Wireless Sensor Networks
N. A. M. Alduais (2017)
10.1109/ACCESS.2019.2926209
RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors
N. A. M. Alduais (2019)
10.1007/978-3-662-45283-7_17
Online Anomaly Detection Method Based on BBO Ensemble Pruning in Wireless Sensor Networks
Zhiguo Ding (2014)
10.1007/978-981-32-9343-4_3
Data Reduction Using NMF for Outlier Detection Method in Wireless Sensor Networks
Oussama Ghorbel (2019)
Performance Analysis of Improved Congestion Control Clustering Protocol Using TBA/WDM
Anupriya (2015)
10.1109/IWCMC.2018.8450421
Non-Negative Matrix Factorization (NMF) for outlier detection in Wireless Sensor Networks
Hamoud Alshammari (2018)
Recent Strategies of Data compression in Wireless Sensor Networks
Dr. Shabana Mehfuz (2013)
10.1109/JSEN.2015.2388498
Fast and Efficient Outlier Detection Method in Wireless Sensor Networks
Oussama Ghorbel (2015)
10.1016/j.asoc.2013.09.024
Fault detection and identification spanning multiple processes by integrating PCA with neural network
Jing Zhou (2014)
10.3390/s130810087
Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues
M. Rassam (2013)
10.1109/TNNLS.2015.2460991
Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers
Francisco Ortega-Zamorano (2016)
10.1016/j.engappai.2014.01.006
Smart sensor/actuator node reprogramming in changing environments using a neural network model
Francisco Ortega-Zamorano (2014)
10.1109/AINA.2015.168
A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks
Oussama Ghorbel (2015)
Unsupervised Anomaly Detection for Unlabelled Wireless Sensor Networks Data
Nurfazrina Mohd Zamry (2018)
10.1016/j.knosys.2014.01.003
Adaptive and online data anomaly detection for wireless sensor systems
M. Rassam (2014)
10.1016/J.AASRI.2013.10.052
An Efficient Distributed Anomaly Detection Model for Wireless Sensor Networks
Murad A. Rassam (2013)
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