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With the rapid development of IoT, to manage the explosive growth of devices, time synchronization in IoT systems has become crucial. A traditional solution is to designate a trusted time relay to provide time synchronization service for IoT. However, due to internal errors or external attacks, time synchronization services in IoT are facing the single point of failure, leading to severe consequences such as downtime of IoT devices across domains. Some centralized time synchronization methods, such as NTP and PTP, rely on the correctness of the time server (master clock), which is vulnerable due to crashes, power failure or external attacks. Most distributed schemes are oriented towards crash fault-tolerant, but the reality of Byzantine nodes prevents these schemes from working as expected. In this paper, we propose a distributed and verifiable time synchronization scheme based on NTP, trust management and blockchain. we employ the public and verifiable blockchain to identify incorrect time synchronization. As the processes of synchronization are viewed as transactions within our blockchain, we design a consensus mechanism to resist Byzantine nodes based on trust management. The simulation results show that the proposed scheme has achieved our expected efficiency and security goals. [ABSTRACT FROM AUTHOR]

Axle is an important part of high-speed train. The axle is the key component connecting the train wheelset, which has a great impact on the train safety. The health monitoring of axles is very important for the safe and smooth operation of trains. The axle health detection is a complex process of multi-factor coupling, which faces the problems of health model construction. From the perspective of big data visual analysis, this paper helps people find the information behind the big data of high-speed railway axle monitoring, and makes a prediction and analysis of the health status of high-speed railway axle operation. Starting from the present situation of scatter plot presentation of multi-dimensional data visual analysis, this paper proposes a visual analysis and processing method for high-speed train axle health monitoring, aiming at the problems of intensive rendering, visual mutation and trend prediction when drawing large data scatter plot. Firstly, a new method of the axle data fusion model is proposed in this paper, which can effectively clean the axle health monitoring data and construct the data acquisition and expression mode of axle temperature of high-speed train. Then, visualization of axle data and prediction of axle health trend provide a new analysis model for axle health monitoring. In addition, the visual analysis method of scatter density map data can eliminate the dependence of the original complex mechanical model, and can be used to analyze different working conditions and axle types. Compared with the existing axle health monitoring methods, this method has high accuracy and practicability. [ABSTRACT FROM AUTHOR]

Because Unmanned Aerial Vehicle (UAV) image exhibits low positioning accuracy, the accurate registration of the image is required. Since the viewpoint direction, capturing time and shoot height are considerably different between the UAV image and google satellite map, the existing methods cannot match two images accurately. For the registration between the UAV image and google satellite map, a full-automated image registration method was proposed based on deep convolution feature. Such method consists of five steps: automatically reference images downloading, uniform key point extraction, deep convolution features computation, accurately feature matching and image registration. The reference image was downloaded from google map service according to the approximate location and region of the UAV image. The deep convolution feature was extracted using the pre-trained VGG16 model. Finally, many experiments were performed to verify the efficiency of the proposed method, and the results demonstrate that the proposed method is more effective and robust than the existing method. [ABSTRACT FROM AUTHOR]

Due to the penetration of various digital devices in our daily life, the data increases in an explosive way. It is highly agreed that the big data era is coming. Big data has a significant and promising future, and makes it a hot topic in the research community. Meanwhile, big data applications also raise consideration of security, privacy and trust on the networking layer. At present, the access control of cloud is vulnerable in existing schemes which may cause the loss and disclosure of the data easily. In this paper, we propose a secure key management scheme which may be deployed in big data networking to protect user’s data and privacy. In the proposed key management scheme, keys are divided into three layers. In the layered structure, upper keys encrypt lower keys to guarantee the security of keys. In the proposed scheme, big data networking, servers and others can know nothing of user’s key. Data owner can achieve ciphertext sharing to share their information to anyone they just went securely. Hierarchical key management is convenient, secure and efficient, and it can protect user’s private data in big data networking. [ABSTRACT FROM AUTHOR]

With the popularity of mobile social network, more and more people are willing to use a mobile device to accomplish some social activities, such as payment when shopping, product information inquiries, where RFID is a technology which can be good to complete payment functions on mobile devices. However, the widespread use of RFID tags brings the possibility for various security risks and privacy problems. In addition, the storage space, processing capability and power supply of RFID tags are limited. In this paper, in order to reduce the computational cost we present an ultra-lightweight RFID authentication scheme, named ULRAS. ULRAS only uses Bit and XOR operations to prevent the DDOS attack. ULRAS introduces the RR method to make the ULRAS more secure in communication, and uses sub-key and sub-index number into its key update process to achieve the forward security. Compared to the SASI protocol and the Gossamer protocol, ULRAS cost less computation and communication resources and have stronger security. [ABSTRACT FROM AUTHOR]