5 EC Course · MDC, T&R

Designing for Digital Twins

Building a city-scale sensor network that connected the physical and digital world.

MDCMath, Data & ComputingT&RTechnology & Realization

About the course

Designing for Digital Twins explored how complex environments become measurable through sensor data and real-time digital models. Working within a city-scale walkability project, I designed and built a sensor node combining hardware development, embedded programming, and environmental sensing into one integrated system. Beyond the build, the course showed how data quality constrains the insights a digital twin can produce. A system’s value depends entirely on the reliability of what feeds it.

Side-by-side of a CAD render and the physical 3D-printed sensor node mounted outdoors on a green pole

Diagram showing real-world inputs flowing through sensors and an ML model to a walkability score

Five mobile screens showing a walkability map, sensor category tiles, dynamic lighting controls, and a feedback prompt

Two-panel flowchart: left shows user decision flow, right shows sensor data streaming to walkability score coloring

Diagram of the data logger with icons for five sensor types and an example JSON payload

Table listing data sources split across probe sensors, app/UI self-report, and CBS database

Heatmap confusion matrix showing prediction accuracy across five walkability categories