Large-scale mobile tra c analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile tra c is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently o er limited insights. Extracting ne-grained pa erns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile tra c super-resolution technique that overcomes these problems by inferring narrowly localised tra c consumption from coarse measurements. We draw inspiration from image processing and design a deep-learning architecture tailored to mobile networking, which combines Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models. is enables to uniquely capture spatio-temporal relations between tra c volume snapshots routinely monitored over broad coverage areas ('low-resolution') and the corresponding consumption at 0.05 km 2 level ('high-resolution') usually obtained a er intensive computation. Experiments we conduct with a real-world data set demonstrate that the proposed ZipNet(-GAN) infers tra c consumption with remarkable accuracy and up to 100× higher granularity as compared to standard probing, while outperforming existing data interpolation techniques. To our knowledge, this is the rst time super-resolution concepts are applied to large-scale mobile tra c analysis and our solution is the rst to infer ne-grained urban tra c pa erns from coarse aggregates.
The authors of REF combine Zipper Networks (ZipNet) and Generative-Adversarial Networks (GAN) to infer narrowly localized and fine grained traffic generation from coarse measurements.
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
CoNEXT '17
Computer Science