Abstract
To overcome the problem of dimension curse in the processing of predicting indoor location by using the traditional Markov chains, this paper proposes a novel hybrid Markov-LSTM model to predict the indoor user’s next location, which adopt the multi-order Markov chains (k-MCs) to model the long indoor location sequences and use LSTM to reduce dimension through combining multiple first-order MCs. Finally, we conduct comprehensive experiments on the real indoor trajectories to evaluate our proposed model. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.
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References
Gambs, S., Killijian, M.-O., Nunez del Prado Cortez, M.: Next place prediction using mobility markov chains. In: Proceedings of the 1st Workshop on Measurement, Privacy, and Mobility, MPM 2012, ACM (2012) https://doi.org/10.1145/2181196.2181199
Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Show me how you move and i will tell you who you are. Trans. Data Privacy 4(2), 103–126 (2011)
Sha, W., Zhu, Y., Chen, M., Huang, T.: Statistical learning for anomaly detection in cloud server systems: a multi-order markov chain framework. IEEE Trans. Cloud Comput. 6(2), 401–413 (2015)
Yu, X.G., Liu, Y.H., Da, W., Lei, L.Y.: A hybrid markov model based on EM algorithm. In: International Conference on Control (2006)
Peixiao, W., Sheng, W., Hengcai, Z., Feng, L.: Indoor location prediction method for shopping malls based on location sequence similarity. ISPRS Int. J. Geo-Inf. 8(11), 517 (2019). https://doi.org/10.3390/ijgi8110517
Cheng, S., Lu, F., Peng, P., Wu, S.: Short-term traffic forecasting: an adaptive ST-KNN model that considers spatial heterogeneity. Comput. Environ. Urban Syst. pp. S0198971518300140 (2018)
Xia, D., Wang, B., Li, H., Li, Y., Zhang, Z.: A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179, 246–263 (2016)
Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retrieval 1(1–2), 69–90 (1999)
Funding
This project was supported by National Key Research and Development Program of China, (Grant Nos. 2016YFB0502104, 2017YFB0503500), and Digital Fujian Program (Grant No. 2016-23).
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Wang, P., Wu, S., Zhang, H. (2020). Predicting Indoor Location based on a Hybrid Markov-LSTM Model. In: Di Martino, S., Fang, Z., Li, KJ. (eds) Web and Wireless Geographical Information Systems. W2GIS 2020. Lecture Notes in Computer Science(), vol 12473. Springer, Cham. https://doi.org/10.1007/978-3-030-60952-8_4
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DOI: https://doi.org/10.1007/978-3-030-60952-8_4
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