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The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios.

As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. First, V2G networks suffer from serious security threats, such as doubtful authenticity and privacy leakage. Second, the efficiency will decrease significantly due to the massive requirements of authentication. To tackle these problems, this paper proposes a cross-domain authentication scheme for V2G networks based on consortium blockchain and certificateless signature technology. Featuring decentralized, open, and transparent transactions that cannot be tampered with, this scheme achieves good performance on both security and efficiency, which proves to be suitable for V2G scenarios in the smart grid.

While the security of blockchain has been the central concern of both academia and industry since the very start, new security threats continue to emerge, which poses great risks to the blockchain ecosystem.A systematic study was conducted on the most state-of-the-art research on potential security issues of blockchain.Specifically, a taxonomy was developed by considering the blockchain framework as a four-layer system, and the analysis on the most recent attacks against security loopholes in each layer was provided.Countermeasures that can strengthen the blockchain were also discussed by highlighting their fundamental ideas and comparing different solutions.Finally, the forefront of research and potential directions of blockchain security were put forward to encourage further studies on the security of blockchain.

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, a detailed design and evaluation of the system, called SmartGe, is presented, which can identify the pen-holding gesture with smartwatch when writing Chinese and English. We firstly analyze the hand movement and propose a novel handwriting detection algorithm to segment each stroke or letter. Then we recognize the pen-holding gesture using deep convolution neural network(DCNN). To improve system performance in Chinese writing, we connect a vertical stroke and a horizontal stroke for pen-holding gesture recognition. SmartGe provides a convenient and natural way to improve users' writing habits, which is a lightweight system, and extensive experiments confirm its effectiveness and robustness.

In this paper, we propose a novel ant colony optimization algorithm based on improved brainstorm optimization (IBSO-ACO) to solve the vehicle routing problem with soft time windows. Compared with the traditional ant colony algorithm, the proposed IBSO-ACO can better address the local optimum problem, since we have carefully designed an improved brainstorming optimization algorithm to update the solutions obtained by the ant colony algorithm, which enhance the solution diversity and the global search ability. Furthermore, we use the classification method to accelerate the convergence of the proposed algorithm. The extensive experimental results have confirmed that the proposed IBSO-ACO algorithm can achieve a lower routing cost at a high convergence rate than the traditional ant colony algorithm and the simulated annealing ant colony algorithm.

Edge computing plays an increasingly important role in the development of the Internet of Things (IoT). However, the explosive growth of terminal devices and unpredictable data flows make data transmission of edge computing systems challenging. In particular, it is crucial to efficiently transfer data from terminal devices to the edge server. In this paper, to ensure the fairness of network resource utilization and avoid starvation of low priority flows, we present a Collaborative Transmission Optimization Mechanism (CTOM) with priority and fairness. Based on the channel quality, CTOM first differentiates devices that can transmit data directly to the edge server from those that need to communicate with the edge server through relay nodes. To maximize transmission success ratio before deadlines of data flows, we perform flow scheduling based on its priority and allocate different bandwidth to different data flows to ensure fairness. In addition, we introduce the concept of energy harvesting (EH) for IoT devices. Equipped with an EH accessory, EH-enabled IoT devices extract energy from ambient resources, such as solar or radio frequency (RF) signals and thus improve energy efficiency. The simulation results verify that the transmission success ratio, the throughput, the average delay, and the energy consumption of CTOM are significantly improved compared with existing algorithms in the literature.

Music multimedia is one of the more popular types of digital music. This article is based on the hidden Markov model (HMM) and proposed this kind of music multimedia automatic classification method. The method not only analyzes the characteristics of traditional music in detail but also fully considers the important characteristics of other music. At the same time, it uses bagging to train two groups of HMMs and automatically classifies them to achieve a better classification effect. This paper optimizes the variable parameters from different aspects such as model structure, data form, and model change to obtain the optimal HMM parameter value. This method not only considers the prior knowledge of feature words, word frequency, and number of documents but also fuses the meaning of the feature words into the hidden Markov classification model. Finally, by testing the hidden Markov model used in this paper on the music multimedia data set, the experimental results show that the method in this paper can effectively perform automatic classification according to the melody characteristics of music multimedia.

PM2.5-bound toxic metals (TMs) are derived from various sources, and they can cause many adverse health effects on the human body. To effectively reduce the disease burden of TMs by controlling the relative sources, an integrated approach of quality-adjusted life years (QALYs) and source-apportionment (positive matrix factorization, PMF) was proposed and applied to some typical diseases induced by TMs in 2017 in Beijing. The estimation included two parts; first, the number of potentially affected people was calculated based on the source mass contribution from PMF and the inhalation unit risk of TMs; second, the QALYs lost per affected person was calculated based on the disease duration, expected years of life lost (EYLL) and quality of life (QoL) for both affected people and the general population. The results showed that QALYs lost per person for renal cancer (17.3 QALYs), pneumonia (14.4 QALYs), lung cancer (14.2 QALYs), skin cancer (12.7 QALYs) and diabetes mellitus (12.6 QALYs) were higher than those for other diseases. Combined with PMF, the source contributions to the overall burden of typical diseases from the TMs followed the order of coal combustion (50.2%) > vehicle emissions (24.4%) > fuel oil combustion (11.4%) > Cr-related industry (10.9%) > resuspended dust (3.0%). The rank was further compared with that assessed for noncancer and cancer risks, and we verified the reasonability of the QALYs method. For seasonal contributions to coal combustion, winter and spring had the highest contributions, which coincided with the fact that coal was the main fuel for heating in Beijing. The QALYs lost attributed to TMs for coal combustion decreased by 49.1% from 2016 to 2017, which may indicate an effective policy associated with coal control. Overall, the integrated approach was successfully employed for estimating the disease burden induced by TMs from each source and was an effective solution to identify the control rank of sources for TM reduction. Keywords: PM2.5, Toxic metals, QALYs, Positive matrix factorization, Coal combustion

The southern Jiangxi province is located at east Nanling range, which is an important W-Sn metallogenic province of China. The Early Yanshanian Tianmenshan is composed of the main-phase porphyritic biotite granite and the highly differentiated fine-gained biotite granite, intruding in the Lower Cambrian Niujiaohe Formation. The main-phase granite and the late-stage highly differentiated granite emplaced at 152–158 Ma and 152–151 Ma, respectively. The later was in the center of the pluton as a ovalize shape, with a transitional contact with the main-phase granite.