Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

With the rapid development of the cellular network, network planning is increasingly important. Generating large-scale urban cellular traffic contributes to network planning via simulating the behaviors of the planned network. Existing methods fail in simulating the long-term temporal behaviors of cellular traffic while cannot model the influences of the urban environment on the cellular networks. We propose a knowledge-enhanced GAN with multi-periodic patterns to generate large-scale cellular traffic based on the urban environment. First, we design a GAN model to simulate the multi-periodic patterns and long-term aperiodic temporal dynamics of cellular traffic via learning the daily patterns, weekly patterns, and residual traffic between long-term traffic and periodic patterns step by step. Then, we leverage urban knowledge to enhance traffic generation via constructing a knowledge graph containing multiple factors affecting cellular traffic in the surrounding urban environment. Finally, we evaluate our model on a real cellular traffic dataset. Our proposed model outperforms three state-of-art generation models by over 32.77%, and the urban knowledge enhancement improves the performance of our model by 4.71%. Moreover, our model achieves good generalization and robustness in generating traffic for urban cellular networks without training data in the surrounding areas.

The problem of cellular traffic generation in cities without historical traffic data is critical and urgently needs to be solved to assist 5G base station deployments in mobile networks. In this paper, we propose ADAPTIVE, a deep transfer learning framework for city-scale cellular traffic generation through the urban knowledge graph. ADAPTIVE leverages historical data from other cities that have deployed 5G networks to assist cities that are newly deploying 5G networks through deep transfer learning. Specifically, ADAPTIVE can align the representations of base stations in the target city and source city while considering the environmental factors of cities, spatial and environmental contextual relations between base stations, and traffic temporal patterns at base stations. We next design a feature-enhanced generative adversarial network, which is trained based on the historical traffic data and representations of base stations in the source city. By feeding the aligned target city's base station representations into the trained model, we can then obtain the generated traffic data for the target city. Extensive experiments on real-world cellular traffic datasets show that ADAPTIVE generally outperforms state-of-the-art baselines by more than 40% in terms of Jensen-Shannon divergence and root-mean-square error. Also, ADAPTIVE has strong robustness based on the results of various cross-city experiments. ADAPTIVE has been successfully deployed on the 'Jiutian' Artificial Intelligence Platform of China Mobile to support cellular traffic generation and assist in the construction and operation of mobile networks.

Network traffic data facilitates understanding the Internet of Things (IoT) behaviors and improving IoT service quality in the real world. However, large-scale IoT traffic data is rarely accessible, and privacy issues also impede realistic data sharing even with anonymous personal identifiable information. Researchers propose to generate synthetic IoT traffic but fail to cover the multiple services provided by widespread real-world IoT devices. In this work, we take the first step to generate large-scale IoT traffic via a knowledge-enhanced generative adversarial network (GAN) framework, which introduces both the semantic knowledge (e.g., location and environment information) and the network structure knowledge for various IoT devices via a knowledge graph. We use a condition mechanism to incorporate the knowledge and device category for IoT traffic generation. Then, we adopt LSTM and a self-attention mechanism to capture the temporal correlation in the traffic series. Extensive experiment results show that the synthetic IoT traffic datasets generated by our proposed model outperform state-of-art baselines in terms of data fidelity and applications. Moreover, our proposed model is able to generate realistic data by only training on small real datasets with knowledge enhanced.

Internet of Things (IoT) devices are increasingly growing in mobile networks with the ubiquity of various IoT services. They share the same infrastructure with smartphones while having different requirements for communication resources and security defense mechanisms. Distinguishing IoT devices from smartphones has far-reaching implications on effective network design, resource allocation scheme, pricing scheme, etc. In this article, we distinguish between 12 107 IoT devices and 12 693 smartphones in the real world via characterizing their network traffic. The IoT devices fall into five categories, namely, locating, monitoring, portable, point of sale (POS), and vehicle. We analyze the device behaviors from the network domain, physical domain, and time domain, make comparisons between each kind of IoT devices and smartphones, and design effective features based on the distinguishable network behavior characteristics at packet level, traffic level, and mobility level. Then, we train several classifiers based on our feature set to identify different kinds of mobile devices. Specifically, the accuracy of identifying IoT devices from smartphones achieves 95.86%, and the accuracies of distinguishing IoT devices in each category from smartphones are all over 95%. In the trained classifiers, feature importance verifies the discriminability of different network traffic characteristics observed in our multidomain measurement. Our study reveals the network traffic behavior characteristics for IoT devices, and successfully distinguishes them from smartphones, which paves the way for better network design, resource allocation, pricing scheme, and security defense mechanisms.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.