Citation:

Ruchi Agarwal, 2024. Applying Cloud-Scale Analytics to Optimize Industrial IoT Networks for Real-Time Monitoring, ESP Journal of Engineering & Technology Advancements 4(1): 118-121.

Abstract:

The convergence of cloud-scale analytics and Industrial Internet of Things (IIoT) networks has revolutionized real-time monitoring across various industries. By leveraging cloud-scale data platforms, organizations can achieve enhanced data processing, analytics, and decision-making capabilities. This paper presents a comprehensive study on optimizing IIoT networks through cloud-scale analytics for real-time monitoring, focusing on data ingestion, edge computing, and AI-based predictive models. We review existing frameworks and propose an optimized architecture integrating machine learning models, predictive maintenance, and anomaly detection for industrial applications. With references to key technical literature, this paper outlines best practices for the deployment of IIoT networks in cloud-native environments, addressing latency, scalability, and reliability challenges.

References:

[1] Smith, A. et al., "Cloud-Based Analytics for IIoT: Trends and Technologies," Journal of Industrial Informatics, vol. 12, no. 3, 2021.

[2] Johnson, R., "Scaling Analytics for IoT: Edge to Cloud Transitions," IEEE Transactions on Cloud Computing, vol. 8, no. 2, 2020.

[3] Chen, X., "Edge Computing in Industrial IoT: Performance and Challenges," Future Generation Computer Systems, vol. 85, 2021.

[4] Zhang, L. et al., "Data Ingestion in Large-Scale IoT Systems," ACM Transactions on Internet Technology, vol. 17, no. 4, 2019.

[5] Brown, T., "Real-Time Data Streaming in Industrial IoT," IEEE Internet of Things Journal, vol. 7, 2020.

[6] Ahmed, M., "Delta Encoding in IoT for Efficient Data Transmission," Sensors, vol. 18, no. 9, 2019.

[7] Li, W., "Edge-Based Data Filtering in IIoT Systems," International Journal of Sensor Networks, vol. 12, 2020.

[8] Castro, P. et al., "Edge Analytics in Smart Manufacturing," Journal of Manufacturing Systems, vol. 44, 2021.

[9] Kim, S., "Leveraging Edge Computing for Real-Time Analytics in IIoT," Future Internet, vol. 11, 2019.

[10] Gupta, D. et al., "Workload Orchestration in IIoT Edge-Cloud Systems," Journal of Parallel and Distributed Computing, vol. 140, 2020.

[11] Ren, J., "Dynamic Orchestration for IIoT Networks Using Cloud Controllers," IEEE Transactions on Network and Service Management, vol. 17, 2020.

[12] Zhao, H., "Predictive Maintenance in IIoT: Challenges and Solutions," IEEE Transactions on Industrial Informatics, vol. 16, no. 5, 2020.

[13] Wang, J. et al., "Deep Learning for Predictive Maintenance in IIoT," Journal of Computational Science, vol. 45, 2021.

[14] Kumar, S., "LSTM Networks for Predictive Maintenance in Industrial Settings," IEEE Access, vol. 9, 2021.

[15] Patel, A., "Feature Engineering in IIoT Predictive Models," Journal of Big Data Analytics, vol. 10, 2020.

[16] Tang, L., "Cloud-Based Training of Predictive Models for IIoT," Computers in Industry, vol. 101, 2020.

[17] Fischer, J. et al., "Unsupervised Learning for Anomaly Detection in IIoT Systems," Pattern Recognition Letters, vol. 131, 2020.

[18] Li, M., "K-Means Clustering for Anomaly Detection in IIoT Networks," Journal of Internet Services and Applications, vol. 11, 2020.

[19] Oliver, P. et al., "Real-Time Anomaly Detection in IIoT Using Isolation Forests," IEEE Access, vol. 8, 2020.

[20] Shen, Z., "Probabilistic Models for Anomaly Detection in Industrial IoT," Sensors, vol. 19, no. 9, 2019.

Keywords:

Cloud-Scale Analytics, Industrial IoT, Real-Time Monitoring, Predictive Maintenance, Edge Computing, Machine Learning, Data Ingestion.