Synthetic generation of cellular network positioning data

The flow of data coming from wireless telecommunication devices enables a novel classes of applications of high societal and economic impact. However, to make this flow of data useful, techniques for the discovery of consumable and concise knowledge out of these raw data have to be developed. Within the long term goal of devising knowledge discovery and analysis methods for trajectories of moving objects, this paper focuses on providing a system to build benchmark datasets for cellular devices positioning data, that typically will not be easily publicly available for scientific research. We called this system CENTRE (CEllular Network Trajectories Reconstruction Environment), and it aims at randomly generating movement data of users through cellular network by simulating semantic-based movement behaviours from a setting of user parameters. CENTRE allows to combine user preferences which may influence the random distributions, domain semantics such as those depending on the cartography and by interesting geo-referenced objects or spatial constraints. The system is composed by three components, namely the Synthetic Trajectories Generation, able to generate possible objects behaviour on a specific space, the Logs generation, which is designed to take into account the various network technological requirements and the Approximated Trajectories Reconstruction which performs the reconstruction taking into account the approximation of the data.