[Retracted] Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Abstract

Low-cost monitoring and automation solutions for smart grids have been made viable by recent advancements in embedded systems and wireless sensor networks (W.S.N.s). A well-designed smart network of subsystems and metasystems known as a “smart grid” is aimed at enhancing the conventional power grid’s efficiency and guaranteeing dependable energy delivery. A smart grid (S.G.) requires two-way communication between utility providers and end users in order to accomplish its aims. This research proposes a novel technique in enhancing the smart grid security and industry fault detection using a wireless sensor network with deep learning architectures. The smart grid network security has been enhanced using a blockchain-based smart grid node routing protocol with IoT module. The industrial analysis has been carried out based on monitoring for fault detection in a network using Q-learning-based transfer convolutional network. The experimental analysis has been carried out in terms of bit error rate, end-end delay, throughput rate, spectral efficiency, accuracy, M.A.P., and RMSE. The proposed technique attained bit error rate of 65%, end-end delay of 57%, throughput rate of 97%, spectral efficiency of 93%, accuracy of 95%, M.A.P. of 55%, and RMSE of 75%. This proposed paradigm is advantageous for the operation of smart grids for increased security and industrial fault detection across the network because security is the biggest barrier in smart grid implementation.

A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network

“…The study provided simulation findings that showed how the suggested strategy might enhance the precision and dependability of IoT health systems. Refs [17,18] proposed an Internet-of-Things (IoT)-enabled data-fusion method for sleep-healthcare applications that integrates data from multiple sources including wearable devices, smartphones, and environmental sensors. The proposed method uses a deep learning-based approach to extract features from the raw data and generate sleep-related metrics.…”