Compressive sensing in wireless sensor network for poultry acoustic monitoring
Abstract
Keywords: wireless sensor network, compressive sensing, poultry acoustic monitoring, poultry sound data, power consumption, acoustic data compression
DOI: 10.3965/j.ijabe.20171002.2148
Citation: Xuan C Z, Wu P, Zhang L N, Ma Y H, Liu Y Q, Maksim. Compressive sensing in wireless sensor network for poultry acoustic monitoring. Int J Agric & Biol Eng, 2017; 10(2): 94–102.
Keywords
Full Text:
PDFReferences
Chung Y, Oh S, Lee J, Park D, Chang H H, Kim S. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors, 2013; 13(10): 12929–12942.
Lee C H, Chou C H, Han C C, Huang R Z. Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis. Pattern Recognition Letters, 2006; 27(2): 93–101.
Xuan C Z, Wu P, Ma Y H, Zhang L N, Han D, Liu Y Q. Vocal signal recognition of ewes based on power spectrum and formant analysis method. Transactions of the CSAE, 2015; 31(24): 219–224. (in Chinese)
Cheng J K, Sun Y H, Ji L Q. A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines. Pattern Recognition, 2010; 43: 3846–3852.
Gunasekaran S, Revathy K. Automatic recognition and retrieval of wild animal vocalizations. International Journal of Computer Theory and Engineering, 2011; 3(1): 136–140.
Xuan C Z, Wu P, Zhang L N, Ma Y H, Zhang Y A, Wu J. Study on feature parameter extraction and recognition method of sheep cough sound. Transactions of the CSAM, 2016; 47(3): 342–348. (in Chinese)
Xuan C Z, Ma Y H, Wu P, Zhang L N, Hao M, Zhang X Y. Behavior classification and recognition for facility breeding sheep based on acoustic signal weighted feature. Transactions of the CSAE, 2016; 32(19): 195–202. (in Chinese)
Guo X M, Zhao C J. Propagation model for 2.4 GHz wireless sensor network in four-year-old young apple orchard. Int J Agric & Biol Eng, 2014; 7(6): 47–53.
Diaz S E, Perez J C, Mateos A C, Marinescu M C, Guerra B B. A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Computers and Electronics in Agriculture, 2011; 76: 252–265.
Kwong M H, Wu T T, Goh H G, Sasloglou K, Stephen B, Glover I. Practical considerations for wireless sensor networks in cattle monitoring applications. Computers and Electronics in Agriculture, 2012; 81(1): 33–44.
David L N, Azizi H, Fitri M R. Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 2014; 105: 83–94.
Ji Y H, Jiang Y Q, Li T, Zhang M, Sha S, Li M Z. An improved method for prediction of tomato photosynthetic rate based on WSN in greenhouse. Int J Agric & Biol Eng, 2016; 9(1): 146–152.
Lin Z C, Schaar M. Autonomic and distributed joint routing and power control for delay-sensitive applications in multi-hop wireless networks. IEEE Transactions on Wireless Communications, 2011; 10(1): 102–113.
Van H L, Tang X. An efficient algorithm for scheduling sensor data collection through multi-path routing structures. Journal of Network and Computer Applications, 2014; 38(2): 150–162.
Wang J, Ma T, Cho J, Lee S. An energy efficient and load balancing routing algorithm for wireless sensor networks, Computer Science and Information Systems, 2011; 9(8): 991–1007.
Abouei J, Brown J D, Plataniotis K N, Pasupathy S. On the energy efficiency of LT codes in proactive wireless sensor networks. IEEE Transactions on Signal Processing, 2011; 59(3): 1116–1127.
Srisooksai J T, Keamarungsi K, Lamsrichan P, Araki K. Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 2012; 35(1): 37–59.
Mehdi B D, Hamid R A, Mohammad R T. Sound source localization using compressive sensing-based feature extraction and spatial sparsity. Digital Signal Processing, 2013; 23(4): 1239–1246.
Model D, Zibulevsky M. Signal reconstruction in sensor arrays using sparse representations. Signal Processing, 2006; 86(3): 624–638.
Quer G, Masiero R, Pillonetto G, Rossi M, Zorzi M. Sensing, compression, and recovery for WSNs: sparse signal modeling and monitoring framework. IEEE Transactions on Wireless Communications, 2012; 11(10): 3447–3461.
Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008; 25(2): 21–30.
Candes E J, Romberg J, Tan T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006; 52(2): 489–509.
Copyright (c)