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Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of integrity of the state estimation in presence of a novel bad data injection threat. We consider persistent adversaries who inject attack vectors “silently,” i.e., they drift the results of the state estimation gradually in multiple steps, with each step bypassing the bad data detection step of state estimation. Prior works on the topic have shown how an adversary can bypass the bad data detection by constructing attack vectors as linear combinations of the column vectors of the measurement Jacobian matrix. We show here that the attack surface is much broader than implied by this assumption. We demonstrate our attack strategy using realistic load patterns from NYISO in the IEEE 14-bus system. We also propose a detection method that uses the expected energy of normalized residues and a paired t-test, and we show its effectiveness. The generality of the proposed attack and detection strategy implies that it can be used in other cyber-physical systems involving state estimation based on linearized state perturbation. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented that focuses on reference for solutions that leverage techniques and insights from the domains of artificial intelligence, internet of things, edge computing, and big data to address security, reliability, privacy, and quality challenges in industrial applications.

In this article, a new structure for dual-input single-output three-phase inverters with a high voltage gain is presented. The proposed structure is based on the impedance source inverters, in which two independent input sources can share their power to supply a common load by replacing the high-frequency transformers with some inductors. The proposed structure is also a voltage booster and is suitable for applications such as connecting to hybrid renewable energy systems. In the proposed structure, to improve the efficiency and to reduce the cost and structure weight, only one power conversion stage is used. In addition, the number of used semiconductors is lower than previous structures in the literature. As renewable energy sources such as wind and solar have the nature of intermittency, the supply of load in the systems which use them as inputs may be marred by some reliability problems. To this end, supplementary sources and also a fast control algorithm could be used to overcome the reliability problem. The proposed switching method controls the ratio of the power absorbed from the input sources and the dc-link voltage through the control of two independent variables. The reliability of the load supply is improved since the controller computes the parameters very fast. The experimental results on a 310-W prototype confirm the theoretical analysis and the performance of the proposed inverter. [ABSTRACT FROM AUTHOR]