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http://dx.doi.org/10.22156/CS4SMB.2021.11.11.031

Outlier Reduction using C-SCGP for Target Localization based on RSS/AOA in Wireless Sensor Networks  

Kang, SeYoung (Department of Computer Science and Engineering, Korea University)
Lee, Jaehoon (Department of Computer Science and Engineering, Korea University)
Song, JongIn (School of Electronic Engineering and Computer Science, Gwangju Institute of Science and Technology)
Chung, Wonzoo (Department of Artificial Intelligence, Korea University)
Publication Information
Journal of Convergence for Information Technology / v.11, no.11, 2021 , pp. 31-37 More about this Journal
Abstract
In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.
Keywords
Wireless Sensor Networks; Received Signal Strength; Angle of Arrival; Localization; Data Fusion; Outlier Detection;
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