Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions
This work addresses privacy and reproducibility issues in cellular network data for researchers and networking applications, but it is incremental as it builds on existing LSTM and mobility modeling techniques.
The paper tackled the challenges of accessibility, usability, and privacy in cellular network datasets by developing Zen, an LSTM-based framework for generating individual spatiotemporal cellular traffic with interactions, which accurately captures individual and global distributions of real-world data and reproduces daily cellular behaviors for applications like dynamic population tracing and anomaly detection.
Domain-wide recognized by their high value in human presence and activity studies, cellular network datasets (i.e., Charging Data Records, named CdRs), however, present accessibility, usability, and privacy issues, restricting their exploitation and research reproducibility.This paper tackles such challenges by modeling Cdrs that fulfill real-world data attributes. Our designed framework, named Zen follows a four-fold methodology related to (i) the LTSM-based modeling of users' traffic behavior, (ii) the realistic and flexible emulation of spatiotemporal mobility behavior, (iii) the structure of lifelike cellular network infrastructure and social interactions, and (iv) the combination of the three previous modules into realistic Cdrs traces with an individual basis, realistically. Results show that Zen's first and third models accurately capture individual and global distributions of a fully anonymized real-world Cdrs dataset, while the second model is consistent with the literature's revealed features in human mobility. Finally, we validate Zen Cdrs ability of reproducing daily cellular behaviors of the urban population and its usefulness in practical networking applications such as dynamic population tracing, Radio Access Network's power savings, and anomaly detection as compared to real-world CdRs.