In the U.S., energy theft causes about six billion dollar losses to utility companies (UCs) every year. With the smart grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the "smart meters" used in smart grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require users to send their private information, e.g., load profiles or meter readings at certain times, to the UCs, which invades users' privacy and raises serious concerns about privacy, safety, etc. To the best of our knowledge, we are the first to investigate the energy theft detection problem considering users' privacy issues. Specifically, in this paper, utilizing peer-to-peer (P2P) computing, we propose three distributed algorithms to solve a linear system of equations (LSE) for users' "honesty coefficients". Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.
Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines and approaches. This article presents the process that was followed to identify and upgrade one station in an industrial workshop to make it compatible with the more extensive system as it evolves into the Industry 4.0 environment. An information processing kit was developed to upgrade the equipment from an automated machine to an Industry 4.0 station. The kit includes a structure to support the sensor and the data processing unit; this unit consisted of a minicomputer that records the data, graded the performance of the components, and sent the data to the cloud for storage, reporting, and further analysis. The information processing kit allowed the monitoring of the inspection system and improved the quality and speed of the inspection process.
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