A novel audio signal acquisition method for wireless sensor networks

This paper proposes two novel audio signal acquisition methods based on compressed sensing theory, which can perfectly achieve long time, continuous, high-speed acquisition of audio signal and real-time, reliable transmission of high-volume of sampling data in wireless sensor network with the limited resources. Reasonable experiments are designed to verify the effectiveness of the two algorithms,and the experiment results show that the two kinds of audio signal acquisition methods are reasonable, practicable and suitable for the wireless sensor networks.

[1]  Sang Hyuk Son,et al.  Feedback control-based dynamic resource management in distributed real-time systems , 2007, J. Syst. Softw..

[2]  Gerald L. Fudge,et al.  Detecting Signal Structure from Randomly-Sampled Data , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[3]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[4]  Richard G. Baraniuk,et al.  The smashed filter for compressive classification and target recognition , 2007, Electronic Imaging.

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

[6]  Raimond Grimberg,et al.  Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors , 2000 .

[7]  Yaakov Tsaig,et al.  Extensions of compressed sensing , 2006, Signal Process..

[8]  H. Odeberg Distance measures for fuzzy sensor opinions , 1993 .

[9]  Mani B. Srivastava,et al.  Energy efficient sampling for event detection in wireless sensor networks , 2009, ISLPED.

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

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

[12]  J. CandesE.,et al.  Near-Optimal Signal Recovery From Random Projections , 2006 .

[13]  Michael Gastpar,et al.  Power, spatio-temporal bandwidth, and distortion in large sensor networks , 2005, IEEE Journal on Selected Areas in Communications.