Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions

TL;DR

This paper introduces Zen, a framework that models and generates realistic, individual-level cellular traffic data using LSTM-based methods, addressing privacy and usability issues in real-world datasets.

Contribution

Zen is the first comprehensive framework combining LSTM-based traffic modeling, mobility emulation, and infrastructure simulation to generate realistic cellular data traces.

Findings

Zen accurately captures real-world Cdrs distributions.

The mobility model aligns with human movement patterns.

Generated data effectively supports network analysis applications.

Abstract

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.