Exploiting Sparsity in Wireless Sensor Networks for Energy Saving : A Comparative Study

To optimize the communication cost, data aggregation in wireless sensor networks (WSNs) is considered an effective technique for energy-saving. The data aggregation in large scale WSNs inevitably faces many challenging problems such as: energy consumption. Fortunately, most sensing data are spatially and temporally correlated and compressible. Matrix completion, which is, an extension to compressive sensing, is considered as a promising reconstruction scheme to recover having missing with low-energy consumption. This paper proposes a data-recovery scheme which can be caste as a low-rank matrix completion framework. In the proposed methodology, the random access protocol is combined with low-rank matrix completion algorithm to minimize the necessary information that sensors transmit. The results indicate the superiority of the proposed algorithm over compressive sensing ones. AMS subject classification:

[1]  Lei Yao,et al.  A DCT Regularized Matrix Completion Algorithm for Energy Efficient Data Gathering in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[2]  Emmanuel J. Candès,et al.  The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.

[3]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[4]  Thore Graepel,et al.  Kernel Matrix Completion by Semidefinite Programming , 2002, ICANN.

[5]  Michele Zorzi,et al.  IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks , 2013, Ad Hoc Networks.

[6]  Kim-Chuan Toh,et al.  SDPT3 -- A Matlab Software Package for Semidefinite Programming , 1996 .

[7]  Milica Stojanovic,et al.  Random Access Compressed Sensing for Energy-Efficient Underwater Sensor Networks , 2011, IEEE Journal on Selected Areas in Communications.

[8]  S. Yun,et al.  An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .

[9]  Salwa H. El-Ramly,et al.  Compressive Data Recovery in Wireless Sensor Networks - A Matrix Completion Approach , 2015, AISI.

[10]  Shiqian Ma,et al.  Fixed point and Bregman iterative methods for matrix rank minimization , 2009, Math. Program..

[11]  Alexandros G. Fragkiadakis,et al.  Joint compressed-sensing and matrix-completion for efficient data collection in WSNs , 2013, 2013 IEEE 18th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[12]  Emmanuel J. Candès,et al.  Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..

[13]  Jun Sun,et al.  Compressive data gathering for large-scale wireless sensor networks , 2009, MobiCom '09.

[14]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[15]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[16]  J. Acimovic,et al.  Adaptive distributed algorithms for power-efficient data gathering in sensor networks , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[17]  Baochun Li,et al.  A Distributed Framework for Correlated Data Gathering in Sensor Networks , 2008, IEEE Transactions on Vehicular Technology.

[18]  Jie Wu,et al.  Maximum network lifetime in wireless sensor networks with adjustable sensing ranges , 2005, WiMob'2005), IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, 2005..

[19]  Junbin Gao,et al.  Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling , 2014, Sensors.

[20]  Olgica Milenkovic,et al.  SET: An algorithm for consistent matrix completion , 2009, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  François Ingelrest,et al.  SensorScope: Application-specific sensor network for environmental monitoring , 2010, TOSN.

[22]  Inderjit S. Dhillon,et al.  Guaranteed Rank Minimization via Singular Value Projection , 2009, NIPS.

[23]  Faramarz Fekri,et al.  Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions , 2006, IPSN.

[24]  Trevor F. Cox,et al.  Metric multidimensional scaling , 2000 .

[25]  V.K. Goyal,et al.  Compressive Sampling and Lossy Compression , 2008, IEEE Signal Processing Magazine.

[26]  Shiqian Ma,et al.  Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization , 2009, Found. Comput. Math..

[27]  Chadi Assi,et al.  Compressive data gathering using random projection for energy efficient wireless sensor networks , 2014, Ad Hoc Networks.

[28]  Nathan Srebro,et al.  Learning with matrix factorizations , 2004 .

[29]  B. Sidda Reddy,et al.  International Journal of Applied Engineering Research , 2018 .

[30]  Lars Elden,et al.  Matrix methods in data mining and pattern recognition , 2007, Fundamentals of algorithms.

[31]  Xiuzhen Cheng,et al.  Robust Compressive Data Gathering in Wireless Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[32]  Athanasios V. Vasilakos,et al.  CDC: Compressive Data Collection for Wireless Sensor Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[33]  Yoram Bresler,et al.  ADMiRA: Atomic Decomposition for Minimum Rank Approximation , 2009, IEEE Transactions on Information Theory.

[34]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..

[35]  Qiang Ye,et al.  STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[36]  Chang Wen Chen,et al.  Correlated data gathering in wireless sensor networks based on distributed source coding , 2008, Int. J. Sens. Networks.

[37]  Antonio Ortega,et al.  Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[38]  John Wright,et al.  Dense Error Correction Via $\ell^1$-Minimization , 2010, IEEE Transactions on Information Theory.

[39]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[40]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[41]  Yin Zhang,et al.  Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm , 2012, Mathematical Programming Computation.

[42]  Martin Vetterli,et al.  DASS: Distributed Adaptive Sparse Sensing , 2013, IEEE Transactions on Wireless Communications.

[43]  Wenyu Liu,et al.  Efficient Data Collection with Sampling in WSNs: Making Use of Matrix Completion Techniques , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[44]  Maytham Safar,et al.  PECA: Power Efficient Clustering Algorithm for Wireless Sensor Networks , 2011, Int. J. Inf. Technol. Web Eng..

[45]  Zhu Han,et al.  Compressive Sensing for Wireless Networks: Preface , 2013 .