Editing Introduction to Edge Computing (section)

===1.3.2 Industrial IoT (IIoT) & Manufacturing ===
Factories and industrial plants increasingly integrate IoT devices for automation, quality control, and predictive maintenance. These operations require real-time decision-making to minimize downtime and optimize efficiency.
==== Visual Quality Inspection: ====
High-resolution cameras capture product images. Instead of uploading gigabytes of images to the cloud, edge devices run AI models to catch defects immediately—critical for high-speed production lines.

==== Digital Twins: ====
A digital twin is a real-time digital replica of a physical asset or system. In industrial settings, edge devices gather data from machines and feed it into a simulated model, which mirrors the behavior of the real-world counterpart. This enables operators to monitor performance, diagnose issues, and even simulate future scenarios on the fly. Edge computing makes these digital twins more responsive, as data is processed locally, offering real-time feedback and facilitating predictive maintenance and operational efficiency.

==== Worker Safety: ====
Industrial environments often pose risks to workers due to hazardous machinery or toxic substances. Edge computing is employed in wearable technologies like smart helmets and vests that monitor workers’ vital signs, movements, and environmental conditions such as gas levels. When abnormalities are detected, edge devices can immediately send alerts to supervisors or trigger emergency protocols. This real-time processing capability is essential for accident prevention and ensures rapid response to potential threats.