AI-Oriented Data Engineering Solutions for Large-Scale Industrial IoT Applications

Authors

  • Narendra Devarasetty Anna University 12, Sardar Patel Rd, Anna University, Guindy, Chennai, Tamil Nadu 600025, India Author

Keywords:

Industrial Internet of Things (IIoT), AI-oriented data engineering, Predictive maintenance, Anomaly detection, Distributed computing, Real-time data processing.

Abstract

The advent of Industrial Internet of Things (IIoT) has revolutionized modern industries by enabling 
real-time monitoring, data collection, and automation across large-scale operations. However, the 
sheer volume, velocity, and variety of data generated in IIoT ecosystems present significant 
challenges for data processing and analytics. This paper explores AI-oriented data engineering 
solutions tailored to the demands of large-scale IIoT applications. By integrating machine learning 
models, distributed computing frameworks, and data pipelines, this study proposes a 
comprehensive approach to optimizing data management and predictive analytics in industrial 
environments. The solutions focus on data preprocessing, real-time stream processing, anomaly 
detection, and predictive maintenance, offering strategies to enhance operational efficiency, 
reliability, and scalability in IIoT systems. Key findings demonstrate that AI-driven data 
engineering methods significantly reduce latency, improve predictive model accuracy, and support 
better decision-making processes. The proposed framework underscores the role of artificial 
intelligence in transforming data engineering practices to meet the complex requirements of largescale IIoT networks.

Downloads

Download data is not yet available.

Downloads

Published

2020-08-02

Most read articles by the same author(s)

Similar Articles

1-10 of 253

You may also start an advanced similarity search for this article.