A Green Distributed Signal Reconstruction Algorithm in Wireless Sensor Networks

Environmental consideration provides new trends in wireless communication network known as green communication. The main object of green communication is to save as much as possible the energy consumption of the communication system. In this paper, the authors have investigated the green distributed nonlinear state estimation problem in wireless sensor networks (WSNs), which will be seamlessly integrated with the forthcoming 5G communication system. A distributed signal reconstruction algorithm is proposed by employing compressive sensing and consensus filter to solve sparse signal reconstruction issue in WSNs with energy efficiency considered. In particular, the pseudo-measurement (PM) technology is introduced into the cubature Kalman filter (CKF), and a sparsity constraint is imposed on the nonlinear estimation by CKF. In order to develop a distributed reconstruction algorithm to fuse the random linear measurements from the nodes in WSNs, the PM embedded CKF is formulated into the information form, and then the derived information filter is combined with the consensus filter, while the square-root version is further developed to improve the performance and strengthen power saving capability. The simulation results demonstrate that the sparse signal can be reconstructed with much fewer nodes in decentralized manner and all the nodes can reach a consensus, while providing some attractive benefits to the green 5G communication system.

[1]  Yang Yang,et al.  Network energy saving technologies for green wireless access networks , 2011, IEEE Wireless Communications.

[2]  Ian Postlethwaite,et al.  Cubature information filter and its applications , 2011, Proceedings of the 2011 American Control Conference.

[3]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[4]  Chi K. Tse,et al.  Reconstruction of Chaotic Signals with Applications to Chaos-Based Communications , 2008 .

[5]  Haris Vikalo,et al.  Rao-Blackwellized unscented Kalman filter for nonlinear systems with bandwidth constraints , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  D. Simon.  Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[7]  Fabrizio Granelli,et al.  A Software-Defined Device-to-Device Communication Architecture for Public Safety Applications in 5G Networks , 2015, IEEE Access.

[8]  Lajos Hanzo,et al.  Green radio: radio techniques to enable energy-efficient wireless networks , 2011, IEEE Communications Magazine.

[9]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[10]  C. Karakus,et al.  Analysis of Energy Efficiency of Compressive Sensing in Wireless Sensor Networks , 2013, IEEE Sensors Journal.

[11]  I. Postlethwaite,et al.  Square Root Cubature Information Filter , 2013, IEEE Sensors Journal.

[12]  Haris Vikalo,et al.  Estimating Time-Varying Sparse Signals Under Communication Constraints , 2011, IEEE Transactions on Signal Processing.

[13]  C. K. Michael Tse,et al.  A Class of Stable Square-Root Nonlinear Information Filters , 2014, IEEE Transactions on Automatic Control.

[14]  Tara N. Sainath,et al.  Kalman filtering for compressed sensing , 2010, 2010 13th International Conference on Information Fusion.

[15]  Guangjie Han,et al.  Cross-layer optimized routing in wireless sensor networks with duty cycle and energy harvesting , 2015, Wirel. Commun. Mob. Comput..

[16]  Namrata Vaswani,et al.  PaFiMoCS: Particle Filtered Modified-CS and Applications in Visual Tracking across Illumination Change , 2013, ArXiv.

[17]  Daeyoung Kim,et al.  PGS: Parking Guidance System based on wireless sensor network , 2008, 2008 3rd International Symposium on Wireless Pervasive Computing.

[18]  Marcello Farina,et al.  Distributed Moving Horizon Estimation for Linear Constrained Systems , 2010, IEEE Transactions on Automatic Control.

[19]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[20]  Vincent Yan Fu Tan,et al.  Wireless Compressive Sensing for Energy Harvesting Sensor Nodes , 2013, IEEE Trans. Signal Process..

[21]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[22]  Florentin Wörgötter,et al.  Square-Root Sigma-Point Information Filtering , 2012, IEEE Transactions on Automatic Control.

[23]  Enrico Magli,et al.  Progressive Compressed Sensing and Reconstruction of Multidimensional Signals Using Hybrid Transform/Prediction Sparsity Model , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[24]  Michel Verhaegen,et al.  Nonlinear Compressive Particle Filtering , 2013, 52nd IEEE Conference on Decision and Control.

[25]  Guangjie Han,et al.  Analysis of Energy-Efficient Connected Target Coverage Algorithms for Industrial Wireless Sensor Networks , 2017, IEEE Transactions on Industrial Informatics.

[26]  Xiaoying Gan,et al.  Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach , 2015, IEEE Transactions on Parallel and Distributed Systems.

[27]  Guangjie Han,et al.  A grid-based joint routing and charging algorithm for industrial wireless rechargeable sensor networks , 2016, Comput. Networks.

[28]  Thomas Strohmer,et al.  Measure What Should be Measured: Progress and Challenges in Compressive Sensing , 2012, ArXiv.

[29]  Yingmin Jia,et al.  Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems , 2012, IEEE Transactions on Automatic Control.

[30]  Deok-Jin Lee,et al.  Nonlinear Estimation and Multiple Sensor Fusion Using Unscented Information Filtering , 2008, IEEE Signal Processing Letters.

[31]  Reza Olfati-Saber,et al.  Kalman-Consensus Filter : Optimality, stability, and performance , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[32]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[33]  Pini Gurfil,et al.  Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms , 2010, IEEE Transactions on Signal Processing.

[34]  Joseph J. LaViola,et al.  On Kalman Filtering With Nonlinear Equality Constraints , 2007, IEEE Transactions on Signal Processing.

[35]  Joost-Pieter Katoen,et al.  Computing Optimal Schedules of Battery Usage in Embedded Systems , 2010, IEEE Transactions on Industrial Informatics.

[36]  Ning Wang,et al.  Survey on Wireless Sensor Networks Agricultural Environment Information Monitoring , 2012 .

[37]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[38]  Andreas Willig,et al.  Recent and Emerging Topics in Wireless Industrial Communications: A Selection , 2008, IEEE Transactions on Industrial Informatics.