Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks

Wireless sensor networks produce immense sensor readings within a report interval to the sink. So transfer of information in a resource constrained wireless environment is difficult. Compressive sensing overcomes the resource constrains in wireless environment by exploiting sparsity in transfer with fewer measurement and recovery of original signal. In this research Intelligent Neighbor Aided Compressive Sensing (INACS) scheme is proposed for efficient data assembly in spatial and temporal correlated WSNs. Sparse Matrix has been formed with spatial and temporal coordinates for data transfer. In every sensing period, the sensor node just sends the readings within the sensing period to uniquely selected neighbour based on a correlation. The transmission period provides significant improvement with compressed data using INACS with the measurement matrix. Thus INACS provides reduction in number of transmission and higher reconstruction accuracy. INACS has been compared with Compressive wireless sensing for reduction in number of transmissions achieved. The time series analysis with INACS has been done to validate the simultaneous association between number of transmissions and time period.

[1]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[2]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[3]  Irena Orovic,et al.  Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals , 2017, Digit. Signal Process..

[4]  Trac D. Tran,et al.  Fast and Efficient Compressive Sensing Using Structurally Random Matrices , 2011, IEEE Transactions on Signal Processing.

[5]  Xue Liu,et al.  Data Loss and Reconstruction in Wireless Sensor Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[6]  Nazanin Rahnavard,et al.  Adaptive non-uniform compressive sampling for time-varying signals , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).

[7]  Milica Stojanovic,et al.  Random Access Compressed Sensing over Fading and Noisy Communication Channels , 2013, IEEE Trans. Wirel. Commun..

[8]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[9]  Yimin Zhao,et al.  A large class of chaotic sensing matrices for compressed sensing , 2018, Signal Process..

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

[11]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

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

[13]  J. Haupt,et al.  Compressive wireless sensing , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[14]  Naima Kaabouch,et al.  A performance comparison of measurement matrices in compressive sensing , 2018, Int. J. Commun. Syst..

[15]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[16]  Holger Rauhut,et al.  Compressive Sensing with structured random matrices , 2012 .

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

[18]  C GilbertAnna,et al.  Algorithms for simultaneous sparse approximation. Part II , 2006 .

[19]  Yue Dong,et al.  A kind of effective data aggregating method based on compressive sensing for wireless sensor network , 2018, EURASIP Journal on Wireless Communications and Networking.

[20]  Xiao Xue,et al.  Neighbor-Aided Spatial-Temporal Compressive Data Gathering in Wireless Sensor Networks , 2016, IEEE Communications Letters.

[21]  Marian Codreanu,et al.  Distributed Distortion-Rate Optimized Compressed Sensing in Wireless Sensor Networks , 2018, IEEE Transactions on Communications.

[22]  Jared Tanner,et al.  Explorer Compressed Sensing : How Sharp Is the Restricted Isometry Property ? , 2011 .

[23]  Michael B. Wakin,et al.  An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition] , 2008 .

[24]  Dong In Kim,et al.  Compressed Sensing for Wireless Communications: Useful Tips and Tricks , 2015, IEEE Communications Surveys & Tutorials.

[25]  Jianjun Yu,et al.  Topology Control Algorithm Based on Bottleneck Node for Large-Scale WSNs , 2009, 2009 International Conference on Computational Intelligence and Security.

[26]  Jiajia Huang,et al.  Cost-Aware Stochastic Compressive Data Gathering for Wireless Sensor Networks , 2019, IEEE Transactions on Vehicular Technology.