TY - GEN
T1 - Accurate Indoor Positioning Prediction Using the LSTM and Grey Model
AU - Fang, Xuqi
AU - Lu, Fengyuan
AU - Chen, Xuxin
AU - Huang, Xinli
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The indoor positioning prediction technologies are developed to locate and predict actual positions of the objective indoors, and can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone. In this paper, we focus on accurate indoor positioning prediction and introduce an improved prediction model for IoT sensing data based on the LSTM and Grey model. In order to enhance the prediction ability of nonlinear samples in IoT sensing data and improve the prediction accuracy of the model, we propose to incorporate into and utilize the advantages of the LSTM model in dealing with nonlinear time series data of different spans, and the ability of the Grey model in dealing with incomplete information and in eliminating residual errors generated by LSTM. To demonstrate the effectiveness and performance gains of the model, we setup experiments based on the indoor trajectory dataset. Experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy, with the RSME for the next day and the next week being 63.39% and 54.86%, respectively, much lower than that of the conventional models.
AB - The indoor positioning prediction technologies are developed to locate and predict actual positions of the objective indoors, and can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone. In this paper, we focus on accurate indoor positioning prediction and introduce an improved prediction model for IoT sensing data based on the LSTM and Grey model. In order to enhance the prediction ability of nonlinear samples in IoT sensing data and improve the prediction accuracy of the model, we propose to incorporate into and utilize the advantages of the LSTM model in dealing with nonlinear time series data of different spans, and the ability of the Grey model in dealing with incomplete information and in eliminating residual errors generated by LSTM. To demonstrate the effectiveness and performance gains of the model, we setup experiments based on the indoor trajectory dataset. Experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy, with the RSME for the next day and the next week being 63.39% and 54.86%, respectively, much lower than that of the conventional models.
KW - Grey model (GM)
KW - Indoor positioning prediction
KW - Internet of Things
KW - Long short-term memory network (LSTM)
KW - Smart elderly-caring
UR - https://www.scopus.com/pages/publications/85096601975
U2 - 10.1007/978-3-030-62005-9_26
DO - 10.1007/978-3-030-62005-9_26
M3 - 会议稿件
AN - SCOPUS:85096601975
SN - 9783030620042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 357
EP - 368
BT - Web Information Systems Engineering – WISE 2020 - 21st International Conference, Proceedings
A2 - Huang, Zhisheng
A2 - Beek, Wouter
A2 - Wang, Hua
A2 - Zhang, Yanchun
A2 - Zhou, Rui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Web Information Systems Engineering, WISE 2020
Y2 - 20 October 2020 through 24 October 2020
ER -