@inproceedings{6b44af6f09ef430ea8fa72d7d8e0c94d,
title = "Wireless indoor positioning using online machine learning",
abstract = "The indoor positioning system can be applied to smart factories to monitor the location of time-critical items in real-time. It is useful for planning and control in the dynamic manufacturing environment. The challenge of the localization is the non-stationary characteristics of the environment. In this paper, we present a predictive modeling method using online machine learning. The wireless signals from time-critical items can be captured constantly. The online positioning model is built and updated by using the sensor data stream.",
keywords = "Online machine learning, Predictive modeling, Sensor data streams, Wireless indoor positioning",
author = "Sheng Huang and Andri Ashfahani and Mahardhika Pratama",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 ; Conference date: 16-12-2019 Through 19-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICMLA.2019.00303",
language = "English",
series = "Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1885--1890",
editor = "Wani, {M. Arif} and Khoshgoftaar, {Taghi M.} and Dingding Wang and Huanjing Wang and Naeem Seliya",
booktitle = "Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019",
address = "United States",
}