Abstract :

Industrial Internet Of Things (IIoT) Has Transformed Modern Manufacturing By Enabling Real-time Monitoring, Predictive Maintenance, And Intelligent Automation. However, The Massive Volume Of Sensor-generated Data And The Need For Ultra-low Latency Make Traditional Cloud-centric Analytics Increasingly Inadequate. Edge Analytics Has Emerged As A Promising Paradigm To Address These Challenges By Processing Data Near The Source, Reducing Communication Overhead, And Enhancing Operational Responsiveness. This Research Investigates Edge-based Data Analytics For IIoT Systems With A Focus On Performance And Efficiency. The Study Explores Lightweight Machine Learning Models, Distributed Computing Frameworks, And Resource-aware Scheduling Techniques To Support Real-time Decision-making At The Network Edge. Experimental Evaluation Demonstrates Significant Reductions In Latency, Bandwidth Consumption, And Energy Utilization Compared To Cloud-only Solutions, While Maintaining High Accuracy In Anomaly Detection And Equipment Health Monitoring. The Results Confirm That Integrating Edge Analytics Into IIoT Environments Enhances Scalability, Reliability, And Overall Industrial Productivity, Making It A Vital Approach For Next-generation Smart Factories. Keywords: Edge Analytics, Industrial IoT, Real-Time Processing, Smart Manufacturing, Latency Reduction, Edge Computing, Resource Efficiency, Distributed Data Processing, Predictive Maintenance, Smart Factory

Published:

03-7-2023

Issue:

Vol. 23 No. 7 (2023)

Page Nos:

267-273

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite