Improving Rare Events Detection in WSN through Cluster-Based Power Control Mechanism

Rare events detection is one of the main applications in Wireless Sensor Networks (WSN) and is currently a central concern of a vast literature. Compressed Sensing (CS) theory has been proved to be quite adapted to this objective. Although this is not the first work on applying CS to sparse events detection in WSN, it is the first to highly justify the validity of the targets detection and counting problem formulation. In order to enhance the CS recovery capacity in WSN, this work considers an approach based on a coherence reduction of the sensing matrix premised on the transmitted power control (PC). Simulation results prove that, under the constraint of equal power consumption, the detection and counting performance is improved when the proposed power control scheme is employed compared to the case without PC.

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

[2]  Di Guo,et al.  Optimized Local Superposition in Wireless Sensor Networks with T-Average-Mutual-Coherence , 2012 .

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

[4]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[5]  Lin Zhang,et al.  Multiple event detection in wireless sensor networks using compressed sensing , 2011, 2011 18th International Conference on Telecommunications.

[6]  D.W. Bliss,et al.  Path-Loss Characteristics of Urban Wireless Channels , 2009, IEEE Transactions on Antennas and Propagation.

[7]  Zheng Zeng Wireless Sensor Networks and their Applications , 2003 .

[8]  Zhuang Xiaoyan,et al.  Wireless sensor networks based on compressed sensing , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[9]  Yingshu Li,et al.  Sparse target counting and localization in sensor networks based on compressive sensing , 2011, 2011 Proceedings IEEE INFOCOM.

[10]  Benoit Geller,et al.  A Low Complexity Block Turbo Decoder Architecture , 2016 .

[11]  Xiuzhen Cheng,et al.  Fault tolerant target tracking in sensor networks , 2009, MobiHoc '09.

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

[13]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[14]  Benoit Geller,et al.  Near Optimum Low Complexity Smoothing Loops for Dynamical Phase Estimation—Application to BPSK Modulated Signals , 2009, IEEE Transactions on Signal Processing.

[15]  Yonina C. Eldar,et al.  Structured Compressed Sensing: From Theory to Applications , 2011, IEEE Transactions on Signal Processing.

[16]  Nidhi Patel,et al.  WIRELESS SENSOR NETWORK USING ZIGBEE , 2013 .

[17]  Benoit Geller,et al.  A low complexity block turbo decoder architecture - [transactions letters] , 2008, IEEE Transactions on Communications.

[18]  Zhifeng Zhao,et al.  On the application of compressed sensing in communication networks , 2010, 2010 5th International ICST Conference on Communications and Networking in China.

[19]  Li Li,et al.  The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[20]  Zhu Han,et al.  Sparse event detection in wireless sensor networks using compressive sensing , 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

[21]  R. Nowak,et al.  Compressed Sensing for Networked Data , 2008, IEEE Signal Processing Magazine.