Advanced Principal Component-Based Compression Schemes for Wireless Sensor Networks

This article proposes two models that improve the Principal Component-based Context Compression (PC3) model for contextual information forwarding among sensor nodes in a Wireless Sensor Network (WSN). The proposed models (referred to as iPC3 and oPC3) address issues associated with the control of multivariate contextual information transmission in a stationary WSN. Because WSN nodes are typically battery equipped, the primary design goal of the models is to optimize the amount of energy used for data transmission while retaining data accuracy at high levels. The proposed energy conservation techniques and algorithms are based on incremental principal component analysis and optimal stopping theory. iPC3 and oPC3 models are presented and compared with PC3 and other models found in the literature through simulations. The proposed models manage to extend the lifetime of a WSN application by improving energy efficiency within WSN.

[1]  H. Robbins,et al.  A Martingale System Theorem and Applications , 1961 .

[2]  G. Haggstrom Optimal Stopping and Experimental Design , 1966 .

[3]  L. Dubins,et al.  OPTIMAL STOPPING WHEN THE FUTURE IS DISCOUNTED , 1967 .

[4]  David Siegmund,et al.  Great expectations: The theory of optimal stopping , 1971 .

[5]  P. Moerbeke On optimal stopping and free boundary problems , 1973, Advances in Applied Probability.

[6]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal stopping rules , 1977 .

[7]  D. H. D. West Updating mean and variance estimates: an improved method , 1979, CACM.

[8]  E. Oja,et al.  On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .

[9]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[10]  David Siegmund,et al.  The theory of optimal stopping , 1991 .

[11]  John W. Auer,et al.  Linear algebra with applications , 1996 .

[12]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[13]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[14]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[15]  Hillol Kargupta,et al.  Distributed Clustering Using Collective Principal Component Analysis , 2001, Knowledge and Information Systems.

[16]  A. Manjeshwar,et al.  TEEN: a routing protocol for enhanced efficiency in wireless sensor networks , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[17]  D.P. Agrawal,et al.  APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[18]  T. Kleinow Testing continuous time models in financial markets , 2002 .

[19]  Jan M. Rabaey,et al.  Energy aware routing for low energy ad hoc sensor networks , 2002, 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No.02TH8609).

[20]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[21]  Ales Leonardis,et al.  Incremental PCA for on-line visual learning and recognition , 2002, Object recognition supported by user interaction for service robots.

[22]  Deborah Estrin,et al.  Impact of network density on data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[23]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[24]  Juyang Weng,et al.  Candid Covariance-Free Incremental Principal Component Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Yu Hen Hu,et al.  Vehicle classification in distributed sensor networks , 2004, J. Parallel Distributed Comput..

[26]  Bruce H. Krogh,et al.  Energy-efficient surveillance system using wireless sensor networks , 2004, MobiSys '04.

[27]  Andreas Klappenecker,et al.  Energy efficient data management for wireless sensor networks with data sink failure , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[28]  Yunfeng Zhang,et al.  Interactive sensor network data retrieval and management using principal components analysis transform , 2006 .

[29]  Yang Xiao,et al.  A Survey of Energy-Efficient Scheduling Mechanisms in Sensor Networks , 2006, Mob. Networks Appl..

[30]  Ling Huang,et al.  In-Network PCA and Anomaly Detection , 2006, NIPS.

[31]  Sajal K. Das,et al.  Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks , 2006, IEEE Transactions on Computers.

[32]  Gianluca Bontempi,et al.  Unsupervised and supervised compression with principal component analysis in wireless sensor networks , 2007 .

[33]  Sylvain Raybaud,et al.  Distributed Principal Component Analysis for Wireless Sensor Networks , 2008, Sensors.

[34]  Mohamed Medhat Gaber,et al.  Knowledge Discovery from Sensor Data , 2008 .

[35]  Yu Ding,et al.  Collaborative data reduction for energy efficient sensor networks , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[36]  Wen Hu,et al.  Energy efficient information collection in wireless sensor networks using adaptive compressive sensing , 2009, 2009 IEEE 34th Conference on Local Computer Networks.

[37]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[38]  Zhenzhen Ye,et al.  Optimal Stochastic Policies for Distributed Data Aggregation in Wireless Sensor Networks , 2009, IEEE/ACM Transactions on Networking.

[39]  Carlos Mauricio S. Figueiredo,et al.  Multivariate reduction in wireless sensor networks , 2009, 2009 IEEE Symposium on Computers and Communications.

[40]  Bibhudatta Sahoo,et al.  Energy Efficiency in Wireless Network: Through Alternate Path Routing , 2009 .

[41]  A. Cox Optimal Stopping and Applications , 2009 .

[42]  Antonio A. F. Loureiro,et al.  Energy in Wireless Sensor Networks , 2009, Middleware for Network Eccentric and Mobile Applications.

[43]  Jesús Cid-Sueiro,et al.  Optimal Selective Transmission under Energy Constraints in Sensor Networks , 2009, IEEE Transactions on Mobile Computing.

[44]  Jun Sun,et al.  Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering , 2010, IEEE Transactions on Wireless Communications.

[45]  Stathes Hadjiefthymiades,et al.  Optimizing pervasive sensor data acquisition utilizing missing values substitution , 2010, PETRA '10.

[46]  Hamid R. Rabiee,et al.  Reducing the data transmission in Wireless Sensor Networks using the Principal Component Analysis , 2010, 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[47]  Iordanis Koutsopoulos,et al.  Measurement aggregation and routing techniques for energy-efficient estimation in wireless sensor networks , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[48]  Stathes Hadjiefthymiades,et al.  An adaptive data forwarding scheme for energy efficiency in Wireless Sensor Networks , 2010, 2010 5th IEEE International Conference Intelligent Systems.

[49]  Stathes Hadjiefthymiades,et al.  Multisensor data fusion for fire detection , 2011, Inf. Fusion.

[50]  Albert N. Shiryaev,et al.  Optimal Stopping Rules , 2011, International Encyclopedia of Statistical Science.

[51]  Stathes Hadjiefthymiades,et al.  Context Compression: Using Principal Component Analysis for Efficient Wireless Communications , 2011, 2011 IEEE 12th International Conference on Mobile Data Management.

[52]  Antonio Ortega,et al.  Signal compression in wireless sensor networks , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[53]  Stathes Hadjiefthymiades,et al.  PC3: Principal Component-based Context Compression: Improving energy efficiency in wireless sensor networks , 2012, J. Parallel Distributed Comput..

[54]  Michele Zorzi,et al.  Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework , 2012, IEEE Transactions on Wireless Communications.

[55]  Chuang Lin,et al.  Attribute-Aware Data Aggregation Using Potential-Based Dynamic Routing in Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.