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

Abstract

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.