No abstract

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

IEEE 802.11ax Wireless Local Area Networks (WLANs) introduce Orthogonal Frequency Division Multiple Access (OFDMA) physical layer technology to improve throughput in dense scenarios. In order to save power of battery operated stations (STAs), a novel broadcast Target Wake Time (TWT) operation for negotiating wake time between an access point (AP) and a group of STAs is also proposed by making full use of the new capability of uplink OFDMA-based multiuser transmissions. However, if the wake time of each STA which is determined by the offset and wake interval (listen interval) is not properly scheduled, deteriorated throughput and high power consumption occur because of collisions. In this paper, we take the advantage of uplink multiuser transmission with the novel TWT scheduling to maximize throughput. We first investigate the fundamental relationship between throughput and energy efficiency with several key aspects, such as the number of simultaneously active STAs, the number of eligible random access resource units, and the contention window size. We further derive the formulations of throughput and energy efficiency on the listen interval of each STA. Based on the relationship, a TWT-based sleep/wake-up scheduling scheme (TSS) is proposed to improve the throughput by reducing or even cancelling collisions. Simulation results demonstrate the effectiveness in terms of average throughput and energy efficiency. The TSS also makes a practical step towards a collision-free and deterministic access in future WLANs when cooperating with TWT service period scheduling. INDEX TERMS IEEE 802.11ax, WLAN, power conservation, target wake time (TWT), orthogonal frequency division multiple access (OFDMA).