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In this paper, an edge computing system for IoT-based (Internet of Things) smart grids is proposed to overcome the drawbacks in the current cloud computing paradigm in power systems, where many problems have yet to be addressed such as fully realizing the requirements of high bandwidth with low latency. The new system mainly introduces edge computing in the traditional cloud-based power system and establishes a new hardware and software architecture. Therefore, a considerable amount of data generated in the electrical grid will be analyzed, processed, and stored at the edge of the network. Aided with edge computing paradigm, the IoT-based smart grids will realize the connection and management of substantial terminals, provide the real-time analysis and processing of massive data, and foster the digitalization of smart grids. In addition, we propose a privacy protection strategy via edge computing, data prediction strategy, and preprocessing strategy of hierarchical decision-making based on task grading (HDTG) for the IoT-based smart girds. The effectiveness of our proposed approaches has been demonstrated via the numerical simulations.INDEX TERMS Edge computing, IoT-based smart grids, data prediction, artificial intelligence, data privacy protection, cloud computing.

Graphene, as a reinforcement for composite materials, has become a focus recently. However, the dispersion of graphene in composite materials is a problem that has been difficult to solve for a long time, which makes it difficult to produce and use graphene-reinforced composites on a large scale. Herein, methods to improve the dispersion of graphene and dispersion mechanisms that have been developed in recent years are reviewed, and the advantages and disadvantages of various methods are compared and analyzed. On this basis, the dispersion methods and mechanisms of graphene are prospected, which lays the foundation for graphene application and preparation.

Abstract-In this paper, we propose an efficient distributedcertificate-service (DCS) scheme for vehicular networks. The proposed scheme offers flexible interoperability for certificate service in heterogeneous administrative authorities and an efficient way for any onboard units (OBUs) to update its certificate from the available infrastructure roadside units (RSUs) in a timely manner. In addition, the DCS scheme introduces an aggregate batchverification technique for authenticating certificate-based signatures, which significantly decreases the verification overhead. Security analysis and performance evaluation demonstrate that the DCS scheme can reduce the complexity of certificate management and achieve excellent security and efficiency for vehicular communications.

In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN)-based sensor nodes’ authentication method, and the convolution preprocessing neural network (CPNN)-based sensor nodes’ authentication method, have been adopted to implement the PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires few computing resources and has extremely low latency, which enable a lightweight multi-node PHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm and minibatch skill are used to accelerate the training of the neural networks. Simulations are performed to evaluate the performance of each algorithm and a brief analysis of the application scenarios for each algorithm is discussed. Moreover, the experiments have been performed with universal software radio peripherals (USRPs) to evaluate the authentication performance of the proposed algorithms. Due to the trainings being performed on the edge sides, the proposed method can implement a lightweight authentication for the sensor nodes under the edge computing (EC) system in IWSNs.