Compressive sensing in wireless sensor network for poultry acoustic monitoring
Keywords:
wireless sensor network, compressive sensing, poultry acoustic monitoring, poultry sound data, power consumption, acoustic data compressionAbstract
Abstract: A wireless acoustic sensor network was realized using wireless sensor nodes equipped with microphone condensers, in which its sensor nodes were configured to capture poultry sound data and transmit it via the network to a collection point. A high performance computer can process these large volumes of animal audio signals under different behaviors. By performing data signal processing and analyzing the audio signal, poultry sound can be achieved and then transformed into their corresponding behavioral modes for welfare assessment. In this study, compressive sensing algorithm was developed in consideration of the balance between the power saving from compression ratio and the computational cost, and a low power consumption as well as an inexpensive sensor node was designed as the elementary unit of poultry acoustic data collecting and transmission. Then, a Zigbee-based wireless acoustic sensor network was developed to meet the challenges of short transmission range and limited resources of storage and energy. Experimental results demonstrate that the compressive sensing algorithm can improve the communication performances of the wireless acoustic sensor network with high reliability, low packet loss rate and low energy consumption. 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.References
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[5] Gunasekaran S, Revathy K. Automatic recognition and retrieval of wild animal vocalizations. International Journal of Computer Theory and Engineering, 2011; 3(1): 136–140.
[6] 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)
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[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
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[18] 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.
[19] Model D, Zibulevsky M. Signal reconstruction in sensor arrays using sparse representations. Signal Processing, 2006; 86(3): 624–638.
[20] 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.
[21] Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008; 25(2): 21–30.
[22] 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.
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Published
2017-03-31
How to Cite
Chuanzhong, X., Pei, W., Lina, Z., Yanhua, M., Yanqiu, L., & ksim, M. (2017). Compressive sensing in wireless sensor network for poultry acoustic monitoring. International Journal of Agricultural and Biological Engineering, 10(2), 94–102. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2148
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Information Technology, Sensors and Control Systems
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