Systematic Survey Analysis of the Application of Artificial Intelligence Base Network on Grid Computing Techniques

Jimmy  Nerat Jakawa; Fidelis Gonten; Datti Useni  Emmanuel; Dakur Atiku  Pandok; Ponfa Canfa  Maikano

doi:10.55537/jistr.v3i3.908

Authors

Jimmy Nerat Jakawa Khal Kum University
Fidelis Gonten Abubakar Tafawa Balewa University Bauchi
Datti Useni Emmanuel Abubakar Tafawa Balewa University
Dakur Atiku Pandok Plateau State University Bokkos, Plateau
Ponfa Canfa Maikano Plateau State Polytechnic Barkin Ladi, Plateau

DOI:

https://doi.org/10.55537/jistr.v3i3.908

Abstract

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern. The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Downloads

Download data is not yet available.

References

S. Rokade and N. Mishra, “A blockchain-based deep learning system with optimization for skin disease classification,” Biomed. Signal Process. Control, vol. 95, 2024, doi: 10.1016/j.bspc.2024.106380.

J. Lee et al., “Blockchain-Based Data Access Control and Key Agreement System in IoT Environment,” Sensors, vol. 23, no. 11, 2023, doi: 10.3390/s23115173.

A. Mohammed and P. Samundiswary, “SecMISS: Secured Medical Image Secret Sharing mechanism for smart health applications,” Vis. Comput., vol. 40, no. 6, pp. 4251 – 4271, 2024, doi: 10.1007/s00371-023-03080-w.

A. Boumaiza and A. Sanfilippo, “A Testing Framework for Blockchain-Based Energy Trade Microgrids Applications,” IEEE Access, vol. 12, pp. 27465 – 27483, 2024, doi: 10.1109/ACCESS.2024.3367999.

A. Kjamilji, “Privacy-Preserving Zero-Sum-Path Evaluation of Decision Tress in Postquantum Industrial IoT,” IEEE Trans. Ind. Informatics, pp. 1–14, 2024, doi: 10.1109/TII.2024.3384523.

M. Adil, M. K. Khan, M. M. Jadoon, M. Attique, H. Song, and A. Farouk, “An AI-Enabled Hybrid Lightweight Authentication Scheme for Intelligent IoMT Based Cyber-Physical Systems,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 5, pp. 2719 – 2730, 2023, doi: 10.1109/TNSE.2022.3159526.

A. Ibrahim, “Low-complexity systolic array structure for field multiplication in resource-constrained IoT nodes,” Ain Shams Eng. J., vol. 14, no. 10, 2023, doi: 10.1016/j.asej.2023.102188.

H. Qin, D. He, Q. Feng, M. K. Khan, M. Luo, and K.-K. R. Choo, “Cryptographic Primitives in Privacy-Preserving Machine Learning: A Survey,” IEEE Trans. Knowl. Data Eng., vol. 36, no. 5, pp. 1919 – 1934, 2024, doi: 10.1109/TKDE.2023.3321803.

M. Zaid, M. W. Akram, A. Rizvi, and S. K. J. Rizvi, “Blockchain based integrity assurance framework for COVID-19 information management & decision making at National Command Operation Center, Pakistan,” Concurr. Comput. Pract. Exp., vol. 35, no. 8, 2023, doi: 10.1002/cpe.7632.

S. Hemavathy and V. S. K. Bhaaskaran, “Arbiter PUF - A Review of Design, Composition, and Security Aspects,” IEEE Access, vol. 11, pp. 33979 – 34004, 2023, doi: 10.1109/ACCESS.2023.3264016.

K. Tsantikidou and N. Sklavos, “Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures,” Cryptography, vol. 8, no. 1, 2024, doi: 10.3390/cryptography8010007.

X. Xu and M. Zhoya, “Path Planning in Multi-AGVs Using a Modified A-star Algorithm,” Int. J. Innov. Educ. Res., vol. 8, no. 5, 2020, doi: 10.31686/ijier.vol8.iss5.2342.

R. Liu and J. Pan, “CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV,” IEEE Internet Things J., vol. 11, no. 1, pp. 1080 – 1095, 2024, doi: 10.1109/JIOT.2023.3287799.

Z. Hua, Y. Tong, Y. Zheng, Y. Li, and Y. Zhang, “PPGloVe: Privacy-Preserving GloVe for Training Word Vectors in the Dark,” IEEE Trans. Inf. Forensics Secur., vol. 19, pp. 3644 – 3658, 2024, doi: 10.1109/TIFS.2024.3364080.

M. Akhtaruzzaman, M. K. Hasan, S. R. Kabir, S. N. H. S. Abdullah, M. J. Sadeq, and E. Hossain, “HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey,” IEEE Access, vol. 8, pp. 222977–223008, 2020, doi: 10.1109/ACCESS.2020.3040083.

S. Liasi, N. S. Ghiasi, and R. Hadidi, “Smart Grid vs. Intelligent Grid; Artificial Intelligence Takes the Power System to the Next Level,” in 2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI), IEEE, Nov. 2023, pp. 1–6. doi: 10.1109/DTPI59677.2023.10365429.

A. Safari and A. A. Ghavifekr, “Quantum Neural Networks (QNN) Application in Weather Prediction of Smart Grids,” in 2021 11th Smart Grid Conference (SGC), IEEE, Dec. 2021, pp. 1–6. doi: 10.1109/SGC54087.2021.9664117.

M. Kaveh, M. R. Mosavi, D. Martín, and S. Aghapour, “An efficient authentication protocol for smart grid communication based on on-chip-error-correcting physical unclonable function,” Sustain. Energy, Grids Networks, vol. 36, 2023, doi: 10.1016/j.segan.2023.101228.

A. Philips and J. Jayakumar, “An Efficient ARIA-RSA-SHA256 Hybridized Encryption Algorithm for Metering Data in Smart Grid Network Systems,” Int. J. Renew. Energy Res., vol. 13, no. 4, pp. 1467 – 1480, 2023, doi: 10.20508/ijrer.v13i4.13998.g8821.

D. Mishra, S. Rana, C. Goyal, and G. Singh, “FOESG: anonymous session key agreement protocol for fog assisted smart grid communication,” Int. J. Ad Hoc Ubiquitous Comput., vol. 42, no. 3, pp. 137 – 147, 2023, doi: 10.1504/IJAHUC.2023.129388.

B. Cui, W. He, and Y. Cui, “A dynamic C-V2X anonymous authentication and group key agreement protocol,” Int. J. Inf. Secur., 2024, doi: 10.1007/s10207-024-00876-2.

W. Zhang et al., “VSpatial: Enabling Private and Verifiable Spatial Keyword-based Positioning in 6G-oriented IoT,” IEEE J. Sel. Areas Commun., pp. 1–1, 2024, doi: 10.1109/JSAC.2024.3414605.

A. B. Araojo, “Workshop-drawing equirectangular perspectives for VR panoramas with Eq A sketch 360,” in Proceedings of 2021 7th International Conference of the Immersive Learning Research Network, iLRN 2021, 2021. doi: 10.23919/iLRN52045.2021.9459397.

X. Huang, H. Wang, W. Luo, S. Xue, F. Hayat, and Z. Gao, “Prediction of loquat soluble solids and titratable acid content using fruit mineral elements by artificial neural network and multiple linear regression,” Sci. Hortic. (Amsterdam)., vol. 278, 2021, doi: 10.1016/j.scienta.2020.109873.

A. Srivastava et al., “SCAR: Power Side-Channel Analysis at RTL Level,” IEEE Trans. Very Large Scale Integr. Syst., vol. 32, no. 6, pp. 1110 – 1123, 2024, doi: 10.1109/TVLSI.2024.3390601.

S. Saif, P. Das, and S. Biswas, “DHOA-ANFIS: A Hybrid Technique to Detect Routing Attacks in Wireless Body Area Network,” Wirel. Pers. Commun., vol. 133, no. 1, pp. 453 – 480, 2023, doi: 10.1007/s11277-023-10774-w.

R. Ren and J. Su, “A Security-Enhanced and Privacy-Preserving Certificateless Aggregate Signcryption Scheme-Based Artificial Neural Network in Wireless Medical Sensor Network,” IEEE Sens. J., vol. 23, no. 7, pp. 7440 – 7450, 2023, doi: 10.1109/JSEN.2023.3247581.

M. K. Kabier, A. A. Yassin, and Z. A. Abduljabbar, “Towards for Designing Educational System Using Role-Based Access Control,” Int. J. Intell. Eng. Syst., vol. 16, no. 2, pp. 50 – 63, 2023, doi: 10.22266/ijies2023.0430.05.

H. Liu, Z. Liu, X. Wang, and Y. Cai, “Bayes’ Theorem based maritime safety information classifier,” in Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, 2018. doi: 10.1109/CCDC.2018.8407588.

Z. Tong, J. Wang, J. Mei, K. Li, and K. Li, “FedTO: Mobile-Aware Task Offloading in Multi-Base Station Collaborative MEC,” IEEE Trans. Veh. Technol., vol. 73, no. 3, pp. 4352 – 4365, 2023, doi: 10.1109/TVT.2023.3329146.

M. Ashraf et al., “A Hybrid CNN and RNN Variant Model for Music Classification,” Appl. Sci., vol. 13, no. 3, 2023, doi: 10.3390/app13031476.

A. R. Khan and A. K. Jilani, “Privacy-Enhanced Heart Disease Prediction in Cloud-Based Healthcare Systems: A Deep Learning Approach with BlockchainBased Transmission,” Fusion Pract. Appl., vol. 15, no. 1, pp. 98 – 119, 2024, doi: 10.54216/FPA.150109.

C. Zhang, D. Jia, L. Wang, W. Wang, F. Liu, and A. Yang, “Comparative research on network intrusion detection methods based on machine learning,” 2022. doi: 10.1016/j.cose.2022.102861.

N. Khan, J. Zhang, G. A. Mallah, and S. A. Chaudhry, “A Secure and Efficient Information Authentication Scheme for E-Healthcare System,” Comput. Mater. Contin., vol. 76, no. 3, pp. 3877 – 3896, 2023, doi: 10.32604/cmc.2023.032553.

J. Sanchez-Gomez, R. Marin-Perez, R. Sanchez-Iborra, and M. A. Zamora, “MEC-based architecture for interoperable and trustworthy internet of moving things,” Digit. Commun. Networks, vol. 9, no. 1, pp. 270 – 279, 2023, doi: 10.1016/j.dcan.2022.03.028.

I. Altarawni et al., “ENHANCING CLOUD SECURITY BASED ON THE KYBER KEY ENCAPSULATION MECHANISM,” J. Theor. Appl. Inf. Technol., vol. 102, no. 4, pp. 1643 – 1651, 2024, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186648137&partnerID=40&md5=fc13929679045371186b0d98397ed4cc

H. Rafik, A. Ettaoufik, and A. Maizate, “Securing Medical Data Exchange: A Decentralized Approach Based on the e-IPGPChain Framework,” Int. J. Saf. Secur. Eng., vol. 14, no. 3, pp. 815 – 829, 2024, doi: 10.18280/ijsse.140314.

Y. Bao, W. Qiu, X. Cheng, and J. Sun, “Fine-Grained Data Sharing With Enhanced Privacy Protection and Dynamic Users Group Service for the IoV,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 11, pp. 13035 – 13049, 2023, doi: 10.1109/TITS.2022.3187980.

M. Syaifuddin, “Diagnosis Of Cancer Of The Women In The Content Using Bayes Theorem,” J. Inf. Syst. Technol. Res., vol. 1, no. 3, pp. 131–140, Sep. 2022, doi: 10.55537/jistr.v1i3.160.

S. Pfeiffer and N. Tihanyi, “D(HE)at: A Practical Denial-of-Service Attack on the Finite Field Diffie–Hellman Key Exchange,” IEEE Access, vol. 12, pp. 957 – 980, 2024, doi: 10.1109/ACCESS.2023.3347422.

S. Aanjanadevi, S. Aanjankumar, K. R. Ramela, and V. Palanisamy, “Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics,” Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 2351 – 2362, 2023, doi: 10.32604/csse.2023.031893.

K. Ince, “Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT,” Int. J. Inf. Secur., vol. 23, no. 2, pp. 1117 – 1130, 2024, doi: 10.1007/s10207-023-00783-y.