Abstract
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.
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Acknowledgements
This work was supported in part by the National Key Research and Development Plan of China under Grant 2019YFB2012803, in part by the Key Project of Shanghai Science and Technology Innovation Action Plan under Grant 19DZ1100400 and Grant 18511103302, in part by the Key Program of Shanghai Artificial Intelligence Innovation Development Plan under Grant 2018-RGZN-02060, and in part by the Key Project of the “Intelligence plus” Advanced Research Fund of East China Normal University.
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Fang, X., Lu, F., Chen, X., Huang, X. (2020). Accurate Indoor Positioning Prediction Using the LSTM and Grey Model. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_26
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DOI: https://doi.org/10.1007/978-3-030-62005-9_26
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