In this paper, an edge computing system for IoTbased (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 electrical grid will be analyses, processed and stored at the edge of the network. Aided with edge computing paradigm, 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 decisionmaking based on task grading (HDTG) for IoT-based smart girds. The effectiveness of our proposed approaches has been demonstrated via numerical simulations.

Internet of Things (IoT) is becoming an emerging paradigm to achieve ubiquitous connectivity, via massive deployment of physical objects, such as sensors, controllers, and actuators. However, concerns on the IoT security are raised due to the wireless broadcasting nature and the energy constraint of the physical objects. In this paper, we study secure downlink transmission from a controller to an actuator, with the help of a cooperative jammer to fight against multiple passive and noncolluding eavesdroppers. In addition to artificial noise aided secrecy beamforming for secure transmission, cooperative jamming (CJ) is explored to further enhance physical layer security. In particular, we provide a secrecy enhancing transmit design to minimize the secrecy outage probability (SOP), subject to a minimum requirement on the secrecy rate. Based on a strict mathematical analysis, we further characterize the impacts of the main channel quality and the minimum secrecy rate on transmit designs. Numerical results confirm that our design can enhance both security (in terms of SOP) and power efficiency as compared with the approach without CJ.

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

This paper investigated the multiple Unmanned Aerial Vehicle (UAV) relays assisted network in Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which were also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused from dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered with aggregated interference were derived in detail. Additionally, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. Simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

This work focuses on the security risks in the access authentication of Internet of Things (IoT), to provide an optimized classification algorithm of radio frequency fingerprinting (RFF). The novel method is based on coherent integration, multi-resolution analysis and Gaussian SVM. First, we proposed a de-noising algorithm for RFF as the present performance of RFF is seriously affected by the noise. The optimized coherent integration firstly developed in this paper effectively improves the signal-to-noise ratio (SNR) of the waveform without increasing the number of required signals, by which a de-noising processing is performed and the identification accuracy is improved. Then a wavelet based multi-resolution analysis is applied to extract feature points in the waveform that has passed the de-noising optimizer, because the less sample points are needed for the support vector machine (SVM) classification processing, which reduces the computational complexity of SVM compared to the classical SVM classification methods where massive sample points are necessary. Extensive experiments are performed. Simulation result shows that the optimized classification algorithm achieves a high accuracy (exceed 99%) at a relatively low SNR (0 dB), which is the best result compared with other existing methods.

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.