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.

The authentication technology of mobile device users has been studied for decades. To balance security, privacy, and usability, motion sensors-based user authentication methods are widely investigated in recent years. However, existing studies meet the problems such as scarcity of training samples, underutilization of data, poor de-noising ability, insufficient transferability, privacy leakage, and low accuracy. To overcome these difficulties, we propose a system, called TrapCog , with the following capabilities: 1) In the phase of data collection, TrapCog can eliminate man-made noise (mislabeling) through differential training based on down-sampling. 2) In the model training stage, the siamese neural network with Long Short-Term Memory (LSTM) as the sub-network is used to achieve sufficient coverage of sample patterns and the transferability of the model. 3) In the phase of real-world authentication, the privacy of the user is tremendously protected through end-side model deployment and local authentication. Experimental results on a dataset composed of 1,513 users with real-world noise show that TrapCog has high accuracy and strong transferability, which is much better than state-of-the-art studies.

Frequency regulation of wind turbines can improve the stability of the power system. However, it would cause generator torque fluctuation, increasing the risk of fatigue load. Previous research works were restricted to inertial and droop responses. Less attention has been paid to striking a balance between frequency regulation and fatigue load. To overcome these, a hybrid control strategy is proposed to consider both frequency response characteristic and fatigue load mitigation. First, a small signal linearization model is built to reveal the impact of the correlation mechanism of frequency regulation on drive train torque load. Second, a multivariable cost function is constructed to optimize the proportional integral (PI) controller, which combines the total fluctuation with the dispersion of the fatigue load and frequency. Then, a hybrid controller based on PI control optimized by particle swarm optimization algorithm and active disturbance rejection control is designed to restrain rapid frequency changes as well as fatigue torque fluctuation simultaneously. Several experiments are performed to verify the significance of the proposed method under different scenarios. Compared with the existing methods, the proposed hybrid control exhibits superiority in improving frequency response and fatigue load mitigation.

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy‐sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real‐world applications. In this paper, we propose an approach calledP³to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper‐layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.