Spatio-Temporal Position Prediction Model for Mobile Users Based on LSTM

In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the user's mobility. If we mine the user's location data, predicting the user's next location, we can get the user's services to be used. It is convenient for the edge server...

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Veröffentlicht in:2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) S. 967 - 970
Hauptverfasser: Tian, Shasha, Zhang, Xiuguo, Zhang, Yingjun, Cao, Zhiying, Cao, Wei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2019
Schlagworte:
Computational modeling
Data models
Long short term memory
Long Short-Term Memory (LSTM)
Mobile Edge Computing (MEC)
Multi-access edge computing
Prediction algorithms
Predictive models
Principal component analysis
Principal Components Analysis (PCA)
Servers
spatio temporal position prediction
Spatiotemporal phenomena
Trajectory
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Zusammenfassung:In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the user's mobility. If we mine the user's location data, predicting the user's next location, we can get the user's services to be used. It is convenient for the edge server to preload the user's services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatio-temporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long Short-Term Memory) model in the mobile edge computing. Firstly, the time series feature extraction method is used to preprocess the historical location data of the mobile user. Next, the model uses the PCA data dimensionality reduction algorithm to process the data and then uses the LSTM model to predict the next spatiotemporal trajectory point of the mobile user. Finally, using the 17621 user trajectory data of the Geolife GPS trajectory data set, the algorithm is tested and verified. The experimental results show that the SPPM model proposed in this paper has higher prediction accuracy and more accurate prediction position.
DOI:10.1109/ICPADS47876.2019.00146