Citation:

Ruchi Patel , 2023. "Artificial Intelligence-Powered Optimization of Industrial IoT Networks Using Python-Based Machine Learning", ESP Journal of Engineering & Technology Advancements, 3(4): 138-148.

Abstract:

Through sophisticated monitoring and real-time data sharing, industrial Internet of Things (IIoT) networks are revolutionizing smart manufacturing. With the increasing complexity of IIoT infrastructures, traditional methods often fail to deliver fault-resilient and scalable performance. With the goal of improving system dependability and operational safety, this paper presents a method to sensor system defect detection and classification based on deep learning (DL), for the purpose of improving IIoT network defect prediction. Using PyTorch and Scikit-Learn in a high-throughput Python industrial simulation environment. The Long Short-Term Memory (LSTM) model had a better fault detection accuracy of 99.33% than the Random Forest (RF) (99%), Multilayer Perceptron (MLP) (96.6%), and FuzHD++ (92%). According to the confusion matrix, the model showed minimal false positives (FP) and false negatives (FN) with 99.38% accuracy, 99.87% recall, and 99.66% F1-score. The robustness and generalizability of the model were demonstrated by the loss plots and training and validation accuracy, which demonstrated strong convergence with minimal overfitting. This method improves operational uptime and system dependability in IIoT scenarios while simultaneously improving real-time issue detection. Overall, the findings illustrate the efficacy of LSTM-based deep learning in enhancing defect resilience and predictive maintenance in intelligent industrial networks.

References:

[1] N. Patel, “Sustainable Smart Cities : Leveraging IoT and Data Analytics for Energy Efficiency and Urban Development,” J. Emerg. Technol. Innov. Res., vol. 8, no. 3, 2021.

[2] S. Pandya, “Predictive Analytics in Smart Grids : Leveraging Machine Learning for Renewable Energy Sources,” Int. J. Curr. Eng. Technol., vol. 11, no. 6, pp. 677–683, 2021, doi: 10.14741/ijcet/v.11.6.12.

[3] Q. Zhou, T. Zhao, X. Chen, Y. Zhong, and H. Luo, “A Fault-Tolerant Transmission Scheme in SDN-Based Industrial IoT (IIoT) over Fiber-Wireless Networks,” Entropy, vol. 24, no. 2, 2022, doi: 10.3390/e24020157.

[4] P. Pathak, A. Shrivastava, and S. Gupta, “A survey on various security issues in delay tolerant networks,” J Adv Shell Program., vol. 2, no. 2, pp. 12–18, 2015.

[5] Z. Li, F. Fei, and G. Zhang, “Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors,” Sensors, vol. 22, no. 15, Aug. 2022, doi: 10.3390/s22155901.

[6] Y. Liu, Y. Yang, X. Lv, and L. Wang, “A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT,” Math. Probl. Eng., vol. 12, 2013, doi: 10.1155/2013/712028.

[7] C. Kan, H. Yang, and S. Kumara, “Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring,” J. Manuf. Syst., vol. 46, pp. 282–293, 2018.

[8] H. S. Chandu, “A Survey of Memory Controller Architectures: Design Trends and Performance Trade-offs,” Int. J. Res. Anal. Rev., vol. 9, no. 4, pp. 930–936, 2022.

[9] A. Polleri, R. Kumar, M. M. Bron, G. Chen, S. Agrawal, and R. S. Buchheim, “Identifying a Classification Hierarchy Using a Trained Machine Learning Pipeline,” U.S. Patent Application No. 17/303,918, 2022

[10] W. Yu, T. Dillon, F. Mostafa, W. Rahayu, and Y. Liu, “A global manufacturing big data ecosystem for fault detection in predictive maintenance,” IEEE Trans. Ind. Informatics, 2020, doi: 10.1109/TII.2019.2915846.

[11] J. Praveenchandar et al., “IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques,” Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/7516328.

[12] S. Kumar et al., “A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling,” Sensors, vol. 22, no. 2, 2022, doi: 10.3390/s22020517.

[13] M. Hojabri, S. Kellerhals, G. Upadhyay, and B. Bowler, “IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods,” Energies, vol. 15, no. 6, 2022, doi: 10.3390/en15062097.

[14] T.-B. Dang, D.-T. Le, T.-D. Nguyen, M. Kim, and H. Choo, “Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data,” IEEE Internet Things J., vol. 8, no. 20, pp. 15468–15485, 2021, doi: 10.1109/JIOT.2021.3073705.

[15] S. Marathe, A. Nambi, M. Swaminathan, and R. Sutaria, “CurrentSense: A Novel Approach for Fault and Drift Detection in Environmental IoT Sensors,” in Proceedings of the International Conference on Internet-of-Things Design and Implementation, New York, NY, USA: ACM, May 2021, pp. 93–105. doi: 10.1145/3450268.3453535.

[16] S. A. Viktoros, M. K. Michael, and M. M. Polycarpou, “Compact Fault Dictionaries for Efficient Sensor Fault Diagnosis in IoT-enabled CPSs,” in 2020 IEEE International Conference on Smart Internet of Things (SmartIoT), 2020, pp. 236–243. doi: 10.1109/SmartIoT49966.2020.00042.

[17] A. Gaddam and V. Govender, “Detecting Sensor Faults and Outliers in Industrial Internet of Things,” in International Conference on Sensing Technology, 2022, pp. 183–200.

[18] S. U. Jan, Y.-D. Lee, J. Shin, and I. Koo, “Sensor fault classification based on support vector machine and statistical time-domain features,” IEEE Access, vol. 5, pp. 8682–8690, 2017.

[19] R. Tarafdar and Y. Han, “Finding Majority for Integer Elements,” J. Comput. Sci. Coll., vol. 33, no. 5, pp. 187–191, 2018.

[20] L.-S. Lin, Z.-Y. Chen, Y. Wang, and L.-W. Jiang, “Improving Anomaly Detection in IoT-Based Solar Energy System Using SMOTE-PSO and SVM Model,” in Frontiers in Artificial Intelligence and Applications, vol. 360, 2022, pp. 123–131. doi: 10.3233/FAIA220434.

[21] M. Vakili, M. Ghamsari, and M. Rezaei, “Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification,” 2020.

[22] M. Safaei, M. Driss, W. Boulila, E. A. Sundararajan, and M. Safaei, “Global outliers detection in wireless sensor networks: A novel approach integrating time-series analysis, entropy, and random forest-based classification.,” Softw. - Pract. Exp., 2022, doi: 10.1002/spe.3020.

[23] M. A. P. Putra, D. S. Kim, and J. M. Lee, “DB-BiLSTM: Euclidean Distance-Based Sensor Data Prediction for IoT Applications,” Int. Conf. ICT Converg., vol. 2021-Octob, no. December, pp. 814–817, 2021, doi: 10.1109/ICTC52510.2021.9620877.

[24] N. Berjab, H. H. Le, and H. Yokota, “Recovering Missing Data via Top-k Repeated Patterns for Fuzzy-Based Abnormal Node Detection in Sensor Networks,” IEEE Access, vol. 10, pp. 61046–61064, 2022, doi: 10.1109/ACCESS.2022.3181742.

Keywords:

Industrial Internet of Things (IIoT), Fault Detection, Predictive Maintenance, Sensor Networks, Network Optimization, Python, Machine Learning, Intel Berkeley Research Lab Sensor Data.