| Title |
User Mobility Dataset For 5G Networks Based On Gps Geolocation |
| ID_Doc |
60415 |
| Authors |
Bouchelaghem S.; Boudjelaba H.; Omar M.; Amad M. |
| Year |
2022 |
| Published |
IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD, 2022-November |
| DOI |
http://dx.doi.org/10.1109/CAMAD55695.2022.9966906 |
| Abstract |
Geolocation technology is the most exciting area of advancement in 5G, leveraging massive sources of accurate location data to provide users with effective location-positioning services and applications. As research on user mobility prediction is steadily growing in the context of 5G networks, the need for available mobility-related data is of utmost importance to support the development and evaluation of new individual mobility patterns. This paper presents a novel mobility dataset generation method for 5G networks based on users' GPS trajectory data. First, we propose aggregating the user's GPS trajectories and modeling his location history by a mobility graph representing the set of cell base stations he passed through. Second, we implement the proposed modeling approach to build a custom mobility dataset and provide a detailed description of our methodology. The generated dataset relies on mobility traces from the real-world Geolife dataset and contains the mobility graph records of 128 users. Finally, we discuss selected use cases for which we believe our dataset would be valuable. © 2022 IEEE. |
| Author Keywords |
5G; Dataset generation; GPS trajectories; Mobility graph; Smart city |
