Abstract
PINUS, an indoor weighted centroid localization (WCL) method with crowdsourced calibration, is proposed in this paper. It relies on crowdsourcing to do the calibration for WCL to improve localization accuracy without the device diversity problem. Smartphones and Bluetooth Low Energy (BLE) beacon devices are applied to realize PINUS for the sake of design validation and performance evaluation.
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Acknowledgment
This work was supported in part by the Ministry of Science and Technology (MOST), Taiwan, under grant numbers 106-2218-E-008-003-, and 107-2918-I-008-002-. Special thanks go to Tokyo Metropolitan University, Japan, for providing international cooperation research opportunity in 2018.
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Jiang, JR., Subakti, H., Chen, CC., Sakai, K. (2019). PINUS: Indoor Weighted Centroid Localization with Crowdsourced Calibration. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_46
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DOI: https://doi.org/10.1007/978-981-13-5907-1_46
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