Mobile UserActivity Prediction Utilizing LSTM Recurrent Neural Network The demand for mobile services is increasing exponentially and mobile telecommunication operators are facing new challenges such as bandwidth choking and resource allocation issues consequently. One of the most promising methods for overcoming these challenges is using Big Data Analytics on Call Detail Record (CDR). In this paper, a LongShort Term Memory neural network is utilized for user activity prediction for the first time. The real CDR data is preprocessed and used for training the neural network. The accuracy of this prediction is evaluated in normal values and anomalies. The proposed system can predict user activity accurately using a limited amount of training data and its performance in anomalous behaviors is promising. The networks ability to be generalized is also evaluated with the crossdataset test. The neural network is trained with one cells data and predict another cells activity properly. The proposed system is a step forward toward designing a practical and efficient CDR anomaly prediction system.

SpatioTemporal 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 useru0027s mobility. If we mine the useru0027s location data, predicting the useru0027s next location, we can get the useru0027s services to be used. It is convenient for the edge server to preload the useru0027s services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatiotemporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long ShortTerm 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.