Editing User:Farhan (section)

==='''Digital Twins'''===
===='''What Is a Digital Twin?'''====
A Digital Twin is a virtual replica of a physical object, system, or process that is continuously updated with real-time data. It uses sensors, connectivity, and analytics to mirror and simulate the behavior and performance of its physical counterpart. This enables organizations to monitor, diagnose, predict, and optimize operations virtually before applying changes in the real world.

===='''How Digital Twins Relate to IoT'''====
The Internet of Things (IoT) plays a foundational role in enabling Digital Twin technology. IoT devices—such as sensors, actuators, smart meters, and wearables—collect data from the physical environment and stream it into the digital twin model in real time. Here’s how the connection works:
#'''Data Collection:''' IoT sensors gather metrics such as temperature, vibration, pressure, humidity, or motion,
#'''Data Transmission:''' These metrics are transmitted over a network to a central system,
#'''Real-Time Sync:''' The digital twin receives this data to update its model, reflecting the real-time status of the asset,
#'''Insight Generation:''' Analytics, AI/ML models, and simulations applied to the digital twin enable predictive maintenance, performance tuning, and scenario testing without affecting the physical system.

===='''Practical Use Cases'''====
*'''Smart Manufacturing:''' IoT-enabled machinery is mirrored by digital twins to optimize workflows, reduce downtime, and simulate design changes,
*'''Smart Cities:''' Digital twins of buildings, transport systems, or power grids leverage data from IoT sensors to improve efficiency and public safety,
*'''Healthcare:''' Wearables and connected medical devices provide biometric data to patient-specific digital twins for personalized treatment simulations.

===='''Benefits:'''====
* Real-Time Monitoring,
* Predictive Analytics & Maintenance
* Virtual Experimentation
* Enhanced Decision-Making
* Enhanced Decision-Making

This foundation makes it easy to later explore where digital twins should be deployed (edge vs cloud), based on '''latency, bandwidth, data sensitivity''', and '''computational demand'''.


Digital twins are virtual representations of physical objects that are regularly updated with real-time data so as to replicate, predict, and maximize functionality. Digital twins are increasingly applied in manufacturing, healthcare, and smart city projects that enable intelligent cities, where predictive maintenance and systems optimization can be realized.

Digital twins connect physical devices with virtual spaces. Digital twins are run at edge nodes in edge computing to carry out local processing of information to minimize the necessity to send colossal amounts of data to the cloud. The configuration is especially applicable in cases requiring real-time analytics, for example, industrial automation and healthcare monitoring.

'''Diagram 1: Digital Twin Edge Network Architecture'''
[[File:Intro_to_edge.png|500px|thumb|center|Real-Time Edge Adoption]]

This diagram illustrates a layered architecture where virtual twins communicate with physical IoT devices through edge nodes. It showcases intelligent transportation systems, 6G networks, and IoT applications as part of a hierarchical edge-cloud framework.

----