The dataset, provided by Shanghai Telecom, contains more than 7.2 million records of accessing the Interent through 3,233 base stations from 9,481 mobile phones for six months. For example, the following figure shows the distribution of base stations. Each node denotes a base station in Shanghai, China. This dataset could help researchers to evaluate their solution in mobile edge computing topic such as edge server placement, service migration, service recommendation, etc.
As shown in the following table, the Telecom dataset shows 6 parameters such as Month, Data, Start Time, End Time, Base Station Location, Mobile Phone ID. The trajectory of users can be found by the dataset.
The month when one record happens
The date when one record happens
The time when a record starts
The time when a record ends
Base Station Location
The longitude and latitude of the base station where the mobile phones access the Interent
The following figure shows when and where the user accessed the Internet by mobile phone.
The telecom dataset is available free of charge for educational and non-commercial purposes. The Telecom data should be used in any scientific or educational study/research. Redistribution of this data to any other third party is not permited.In exchange, we kindly request that you make available to us the results of running the telecom dataset. You must cite the following papers when using this Telecom dataset.
 S. Wang, Y. Zhao, J. Xu, J. Yuan, C. Hsu, Edge Server Placement in Mobile Edge Computing, Journal of Parallel and Distributed Computing, vol, 127, pp.160-168, 2019. [PDF]
 S. Wang, Y. Zhao, L. Huang, J. Xu, C. Hsu. QoS Prediction for Service Recommendations in Mobile Edge Computing, Journal of Parallel and Distributed Computing,vol. 127, pp.134-144, 2019.
 Y. Guo, S. Wang, A. Zhou, J. Xu, J. Yuan, C. Hsu. User Allocation‐aware Edge Cloud Placement in Mobile Edge Computing, Software: Practice and Experience, vol. 50, no. 5, pp. 489-502, 2020.[PDF] [Sourcecode]
 S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, X. Shen. Delay-aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach, IEEE Transactions on Mobile Computing, https://ieeexplore.ieee.org/document/8924682, 2019[PDF]