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http://dx.doi.org/10.14372/IEMEK.2020.15.5.243

Learning-based Improvement of CFAR Algorithm for Increasing Node-level Event Detection Performance in Acoustic Sensor Networks  

Kim, Youngsoo (Air Force Academy)
Publication Information
Abstract
Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.
Keywords
Wireless sensor network; Acoustic sensor; Event detection; CFAR; Learning;
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1 S. K. Gupta, P. Sinha, "Overview of Wireless Sensor Network: a survey," Telos $3.15{\mu}$, 38mW, 2014.
2 M. A. Alsheikh, S. Lin, D. Niyato, H.P. Tan, "Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications," IEEE Communications Surveys & Tutorials, Vol. 16, No. 4, pp. 1996-2018, 2014.   DOI
3 J. Ding, S. Y. Cheung, C. W. Tan, P. Varaiya, "Signal Processing of Sensor Node Data for Vehicle Detection," Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749), pp. 70-75, 2004.
4 Yim, Sung-Jib, Yoon-Hwa Choi. "An Adaptive Fault-tolerant Event Detection Scheme for Wireless Sensor Networks," Sensors, Vol. 10, No. 3, pp. 2332-2347, 2010.   DOI
5 D. Li, K. D. Wong, Y. H. Hu, A. M. Sayeed, "Detection, Classification, and Tracking of Targets," IEEE signal processing magazine Vol. 19, No. 2, pp. 17-29, 2002.   DOI
6 M. Duarte, Y. H. Hu, "Distance-based Decision Fusion in a Distributed Wireless Sensor Network," Telecommunication Systems, Vol. 26, pp. 339-350, 2004.   DOI
7 P. P. Gandhi, S. A. Kassam, "Analysis of CFAR Processors in Nonhomogeneous Background," IEEE Transactions on Aerospace and Electronic systems, Vol. 24, No. 4, pp. 427-445, 1988.   DOI
8 R. M. Alsina-Pages, F. Alias, J. C. Socoro, F. Orga. "Detection of Anomalous Noise Events on Low-capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping Within an Hybrid WASN," Sensors, Vol. 18, No. 4, pp. 1272, 2018.   DOI
9 Herbert A. Simon, "What We Know about Learning," Journal of engineering education, Vol. 87, No. 4, pp. 343-348, 1998.   DOI
10 LIBSVM Data: Vehicle Classification of SensIT, https://www.openml.org/d/357