In this article, we provide a concise but systematic review on blockchain-enabled cyber-physical systems (CPS). We dissect various blockchain-enabled CPS as reported in the literature in terms of their operations and the features of blockchain that have been used. We identify key common CPS operations that can be enabled by blockchain, and classify them in terms of their time sensitivity and throughput requirements. We also elaborate and classify features of blockchain in terms of different levels of benefits to CPS, including security, privacy, immutability, fault tolerance, interoperability, data provenance, atomicity, automation, data/service sharing, and trust. Finally, we point out two primary open research issues for developing blockchain-enabled CPS, namely, excessive delay in reaching consensus and limited throughput, and outline future research directions.

In recent years, deep learning methods based on a large amount of data have achieved substantial success in numerous fields. However, with increases in regulations for protecting private user data, access to such data has become restricted. To overcome this limitation, federated learning (FL) has been widely utilized for training deep learning models without centralizing data. However, the inaccessibility of FL data and heterogeneity of the client data render difficulty in providing security and protecting the privacy in FL. In addition, the security and privacy anomalies in the corresponding systems significantly hinder the application of FL. Numerous studies have been proposed aiming to maintain the model security and mitigate the leakage of private training data during the FL training phase. Existing surveys categorize FL attacks from a defensive standpoint, but lack the efficiency of pinpointing attack points and implementing timely defenses. In contrast, our survey comprehensively categorizes and summarizes detected anomalies across client, server, and communication perspectives, facilitating easier identification and timely defense measures. Our survey provides an overview of the FL system and briefly introduces the FL security and privacy anomalies. Next, we detail the existing security and privacy anomalies and the methods of detection and defense from the perspectives of the client, server, and communication process. Finally, we address the security and privacy anomalies in non-independent identically distributed cases during FL and summarize the related research progress. This survey aims to provide a systematic and comprehensive review of security and privacy research in FL to help understand the progress and better apply FL in additional scenarios.

Cyber physical systems (CPSs) are rapidly developing, with increasing scale, complexity, and heterogeneity. However, testing CPSs systematically to ensure that they operate with high reliability remains a big challenge. Therefore, it is necessary to summarize existing works and technologies systematically, with the aim of inspiring new inventions for more efficient CPS testing. Accordingly, this paper first investigated the advances in CPS testing methods from ten aspects, including different testing paradigms, technologies, and some non-functional testing methods (including security testing, robust testing, and fragility testing). Then, we further elaborate on the infrastructures of CPS testbeds from the perspectives of their architecture and the corresponding function analyses. Finally, challenges and future research directions are identified and discussed. It can be concluded that future CPS testing should focus more on the combination of different paradigms and technologies for multi-objective by integrating more emerging cutting-edge technologies such as Internet of things, big data, cloud computing, and AI.

The success of deep learning in state-of-charge estimation relies on the assumption that training and test data have the same distribution. However, this assumption is mostly invalid in real-world applications because the battery operating conditions are diverse and considerable battery data are difficult to obtain to train a specific deep estimator for each condition. To solve these problems, a deep transfer neural network (DTNN) with multiscale distribution adaptation (MDA), which generalizes the deep estimator for domain adaptation, is proposed for cross-domain state-of-charge estimation. In this method, DTNN composed of convolutional and bidirectional recurrent neural networks is constructed to learn nonlinear dynamic features of battery measurements from the source and target domains. Then, MDA is developed to minimize the distribution discrepancy of the high-level transferable features between the source and target domains at multiple scales by simultaneously imposing constraint terms on the DTNN layers. These domain-shared features that obey to small discrepancy can enhance the generalizability and robustness of DTNN for target estimation tasks. Through extensive experiments on three battery datasets, the results show that compared with the state-of-the-art transfer learning methods, the adapted DTNN learned with limited battery data achieves the best performance under low-capacity discharge condition and charge-pause-discharge condition.

Driver fatigue is a major cause of traffic accidents. Automatic vision-based driver fatigue recognition is one of the most prospective commercial applications based on facial expression analysis technology. Generally, factors such as noise, illumination effects, image scaling, and redundant data affect the performance of facial expression recognition systems. In this paper, we have proposed an efficient algorithm, which is not only capable of working with multi-scale images but also able to overcome the mentioned obstacles. The proposed framework can be divided into three main phases. In the first step, the input image is converted into four sub-band images by applying a discrete wavelet transform, which preserves the important information of face image. Also, the original image is down-sampled to obtain the image of different sizes. Based on entropy analysis, each image is then further divided into a number of blocks classified as either informative or non-informative blocks. In the second step, the high variance features are selected in a zigzag manner using discrete cosine transform. In the final step, classifiers are trained and tested to accurately classify the expressions into seven generic expression classes. The empirical results suggest that the proposed framework not only effectively utilizes the multi-scale images but also outperforms other similar techniques in terms of classification accuracy rate.

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affects fleet productivity and performance. In this paper, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle’s health. Data from sensors are received on the driver’s smartphone through a microcontroller and, after processing, useful information is displayed on the driver’s mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.