Since the continuous authentication (CA) system based on smartphone sensors has been facing the challenge of the low-data regime under some practical scenarios, which leads to low accuracy of CA, it needs to be solved urgently. To this end, currently, the generative adversarial networks (GAN) provide a powerful method to train the result generative model that could generate very convincing verisimilar data. The framework of the GAN and its variants shed much light on improving the performance of CA. Therefore, in this article, we propose a continuous authentication system on smartphones based on a Wasserstein generative adversarial network (WGAN) for sensor data augmentation, which utilizes accelerometers, gyroscopes, and magnetometers of smartphone sensors to sense phone movements caused by user operation behavior. Specifically, based on sensor data under different user activities, the WGAN is used to create additional data in training data for data augmentation. With the augmented data, we design a convolutional neural network to learn and extract deep features from sensor data, and then use four classifiers of RF, OCSVM, DT, and KNN to train these features. Finally, we train and test on the HMOG dataset, and the results show that the EER of the authentication system is between 3.68% and 6.39% on the sensor data with a time window of 2 s.

Blockchain technology has been widely used in many fields, such as smart cities, smart health care, and smart manufacturing, due to its anonymity, decentralization, and tamper resistance in peer-to-peer (P2P) networks. However, poor scalability has severely affected the widespread adoption of traditional blockchain technology in high-throughput and low-latency applications. Therefore, based on the three-layer architecture, this study presents a variety of solutions to improve the scalability of the blockchain. As the scale of the network expands, one of the most practical ways to achieve horizontal scalability is sharding, where the network is divided into multiple subnetworks to avoid repeated communication overhead, storage, and calculations. This study provides a systematic and comprehensive introduction to blockchain sharding, along with a detailed comparison and evaluation for primarily considered sharding mechanisms. We also provide the detailed calculations and then analyze the characteristics of existing solutions along with our insights.

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