Navigating Industry 4.0 Frontiers: A Scalable and Resilient Next-Generation IoT Framework to Implement Future Advancements in Smart and Adaptive Industrial Systems

Main Article Content

Bireshwar Ganguly, Devashri Kodgire, Samir N. Ajani, Praveen H. Sen, Nilesh Shelke, Anil W.Kale

Abstract

The emergence of Industry 4.0 signifies a paradigm shift in industrial systems, characterized by the amalgamation of digital technologies with tangible operations. The goal of this study is to present a state-of-the-art, scalable, and robust Internet of Things (IoT) framework that will enable future innovations in intelligent and adaptable industrial systems to be seamlessly integrated. Our framework gives scalability first priority in response to Industry 4.0's dynamic nature, which is marked by fast technical evolution and rising connection in order to handle the expanding ecosystem of networked devices. The suggested structure places a strong emphasis on resilience and is designed to resist setbacks and guarantee the continuation of vital industrial processes. Our framework improves industrial systems' intelligence by utilizing edge computing, machine learning techniques, and improved communication protocols. This allows the systems to self-adapt to changing situations. Moreover, it adopts a modular architecture that facilitates interoperability and makes it simple to integrate various devices and technologies. Our IoT framework creates a solid, flexible, and future-proof industrial environment with this all-encompassing strategy, enabling businesses to confidently and effectively traverse Industry 4.0's frontiers.

Article Details

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Author Biography

Bireshwar Ganguly, Devashri Kodgire, Samir N. Ajani, Praveen H. Sen, Nilesh Shelke, Anil W.Kale

[1]Bireshwar Ganguly

2Devashri Kodgire

3Samir N. Ajani

4Praveen H. Sen

5Nilesh Shelke

6Anil W.Kale

 

[1] Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering, Research and Technology, Chandrapur, Maharashtra, India.bireshwar.ganguly@gmail.com

2Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering, Research and Technology, Chandrapur, Maharashtra, India. devashriraich@gmail.com

3Department of Computer Science and Engineering (Data Science), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India, samir.ajani@gmail.com

4Computer Science and Business Systems, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India, senpraveen.it@gmail.com

5Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.

nilesh.shelke@sitnagpur.siu.edu.in

6Department of Computer Engineering, MGM's college of Engineering and Technology,Kamothe,Navi Mumbai, India. anil5474@gmail.com 

 

References

S. Khaf, M. T. Alkhodary and G. Kaddoum, "Partially Cooperative Scalable Spectrum Sensing in Cognitive Radio Networks Under SDF Attacks," in IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8901-8912, 1 June1, 2022, doi: 10.1109/JIOT.2021.3116928.

M. P. Maharani, P. Tobianto Daely, J. M. Lee and D. -S. Kim, "Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach," 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2020, pp. 1880-1882, doi: 10.1109/ICTC49870.2020.9289380.

M. Klymash, O. Hordiichuk-Bublivska, M. Kyryk, L. Fabri and H. Kopets, "Big Data Analysis in IIoT Systems Using the Federated Machine Learning Method," 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 2022, pp. 248-252, doi: 10.1109/TCSET55632.2022.9766908.

V. Obarafor, M. Qi and L. Zhang, "A Review of Privacy-Preserving Federated Learning, Deep Learning, and Machine Learning IIoT and IoTs Solutions," 2023 8th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2023, pp. 1074-1078, doi: 10.1109/ICSIP57908.2023.10270935.

S. K. Kishore, G. Vasukidevi, E. P. C. Prasad, T. R. Patnala, V. P. Reddy and P. B. Chanda, "A Real- Time Machine learning based cloud computing Architecture for Smart Manufacturing," 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 562-565, doi: 10.1109/ICAAIC53929.2022.9792860.

M. D. Choudhry, J. S, B. Rose and S. M. P, "Machine Learning Frameworks for Industrial Internet of Things (IIoT): A Comprehensive Analysis," 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022, pp. 1-6, doi: 10.1109/ICEEICT53079.2022.9768630.

P. Kumar and I. Banerjee, "Attack and Anomaly Detection in IIoT Networks Using Machine Learning Techniques," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-7, doi: 10.1109/ICCCNT56998.2023.10308014.

S. Messaoud, A. Bradai, S. Dawaliby and M. Atri, "Slicing Optimization based on Machine Learning Tool for Industrial IoT 4.0," 2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS), Sfax, Tunisia, 2021, pp. 1-5, doi: 10.1109/DTS52014.2021.9498080.

A. Kanawaday and A. Sane, "Machine learning for predictive maintenance of industrial machines using IoT sensor data," 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2017, pp. 87-90, doi: 10.1109/ICSESS.2017.8342870.

B. Cline, R. S. Niculescu, D. Huffman and B. Deckel, "Predictive maintenance applications for machine learning", 2017 Annual Reliability and Maintainability Symposium (RAMS), pp. 1-7, 2017.

J. S. L. Senanayaka, S. T. Kandukuri, Huynh Van Khang and K. G. Robbersmyr, "Early detection and classification of bearing faults using support vector machine algorithm", 2017 IEEE Workshop on Electrical Machines Design Control and Diagnosis (WEMDCD), pp. 250-255, 2017.

E. H. M. Pena, M. V. O. De Assis and M. L. Proenca, "Anomaly Detection Using Forecasting Methods ARIMA and HWDS", 2013 32nd International Conference of the Chilean Computer Science Society (SCCC), pp. 63-66, 2013.

Z. Chen, Z. Gao, R. Yu, M. Wang and P. Sun, "Macro-level accident fatality prediction using a combined model based on ARIMA and multivariable linear regression", 2016 International Conference on Progress in Informatics and Computing (PIC), pp. 133-137, 2016.

J. Shimada and S. Sakajo, "A statistical approach to reduce failure facilities based on predictive maintenance", 2016 International Joint Conference on Neural Networks (IJCNN), pp. 5156-5160, 2016.