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Sensors 2018, 18(12), 4267; https://doi.org/10.3390/s18124267

Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression

Department of Information and Communication Engineering, Chosun University, Kwangju 501-759, Korea
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Received: 12 November 2018 / Revised: 30 November 2018 / Accepted: 3 December 2018 / Published: 4 December 2018
(This article belongs to the Collection Positioning and Navigation)
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Abstract

Fingerprinting localization approach is widely used in indoor positioning applications owing to its high reliability. However, the learning procedure of radio signals in fingerprinting is time-consuming and labor-intensive. In this paper, an affinity propagation clustering (APC)-based fingerprinting localization system with Gaussian process regression (GPR) is presented for a practical positioning system with the reduced offline workload and low online computation cost. The proposed system collects sparse received signal strength (RSS) data from the deployed Bluetooth low energy beacons and trains them with the Gaussian process model. As the signal estimation component, GPR predicts not only the mean RSS but also the variance, which indicates the uncertainty of the estimation. The predicted RSS and variance can be employed for probabilistic-based fingerprinting localization. As the clustering component, the APC minimizes the searching space of reference points on the testbed. Consequently, it also helps to reduce the localization estimation error and the computational cost of the positioning system. The proposed method is evaluated through real field deployments. Experimental results show that the proposed method can reduce the offline workload and increase localization accuracy with less computational cost. This method outperforms the existing methods owing to RSS prediction using GPR and RSS clustering using APC. View Full-Text
Keywords: affinity propagation clustering; bluetooth low energy; fingerprinting localization; gaussian process regression affinity propagation clustering; bluetooth low energy; fingerprinting localization; gaussian process regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Subedi, S.; Pyun, J.-Y. Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression. Sensors 2018, 18, 4267.

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