RETRACTED: Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques (Retracted Article)

被引:9
作者
Kandasamy, Manivel [1 ]
Anto, S. [2 ]
Baranitharan, K. [3 ]
Rastogi, Ravi [4 ]
Satwik, Gunda [5 ]
Sampathkumar, A. [6 ]
机构
[1] Karnavati Univ, UnitedWorld Sch Computat Intelligence, Gandhinagar 382422, Gujarat, India
[2] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, India
[3] VTU, Alvas Inst Engn & Technol, Dept ECE, Mangalore, Karnataka, India
[4] Koneru Lakshmaiah Educ Fdn, Dept CSE, Vaddeswaram, AP, India
[5] Graph Era Deemed Be Univ, Dept Comp Sci & Engn, Dehra Dun, India
[6] Dambi Dollo Univ, Dept Comp Sci & Engn, Dambi Dollo, Ethiopia
关键词
SCHEME;
D O I
10.1155/2023/3806121
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:13
相关论文
共 28 条
[1]   Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication [J].
Arya, Greeshma ;
Bagwari, Ashish ;
Chauhan, Durg Singh .
IEEE ACCESS, 2022, 10 :9340-9356
[2]  
Banuselvasaraswathy B., 2020, 2020 Proceedings of the International Conference on Communication and Signal Processing (ICCSP), P1472, DOI 10.1109/ICCSP48568.2020.9182400
[3]   Privacy reinforcement learning for faults detection in the smart grid [J].
Belhadi, Asma ;
Djenouri, Youcef ;
Srivastava, Gautam ;
Jolfaei, Alireza ;
Lin, Jerry Chun-Wei .
AD HOC NETWORKS, 2021, 119
[4]   Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence [J].
Chehri, Abdellah ;
Fofana, Issouf ;
Yang, Xiaomin .
SUSTAINABILITY, 2021, 13 (06)
[5]   Stochastic energy management and scheduling of microgrids in correlated environment: A deep learning-oriented approach [J].
Cheng, Tan ;
Zhu, Xiangqian ;
Gu, Xiaoyong ;
Yang, Fan ;
Mohammadi, Mojtaba .
SUSTAINABLE CITIES AND SOCIETY, 2021, 69
[6]   A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System [J].
Ding, Yucheng ;
Ma, Kang ;
Pu, Tianjiao ;
Wang, Xinying ;
Li, Ran ;
Zhang, Dongxia .
ELECTRONICS, 2021, 10 (12)
[7]   Improvised Model for Blockchain in Distributed Cloud Environment [J].
Gawde, Meeraj Mahendra ;
Choudhary, Gaurav ;
Shandilya, Shishir Kumar ;
Rahman, Rizwan Ur ;
Park, Hoonyong ;
You, Ilsun .
CYBERSPACE SAFETY AND SECURITY, CSS 2022, 2022, 13547 :327-341
[8]   An Ultra-Lightweight Data-Aggregation Scheme with Deep Learning Security for Smart Grid [J].
Gope, Prosanta ;
Sharma, Pradip Kumar ;
Sikdar, Biplab .
IEEE WIRELESS COMMUNICATIONS, 2022, 29 (02) :30-36
[9]  
Hoda S. A., 2022, INT J RECENT INNOVAT, V10, P13, DOI [10.17762/ijritcc.v10i5.5548, DOI 10.17762/IJRITCC.V10I5.5548]
[10]   RETRACTED: Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks (Retracted Article) [J].
Jampana, Venkata N. Raju ;
Rao, P. S. V. Ramana ;
Sampathkumar, A. .
ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2021, 2021