Smart Street Lighting for Sustainable City District

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Overview

What this challenge is about.

Smart Street Lighting for Sustainable City District. Beginner-friendly challenge in design. Designing real products under real constraints, earn a blockchain...

CredentialBlockchain-anchored

ShareableLinkedIn-ready

LanguageEnglish

PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Design an IoT-based adaptive street lighting system to reduce energy consumption and improve safety in a city district.

This is not a design exercise. It is the work a product designer does between a brief and a shipped interface. That distinction matters to every hiring manager who has seen candidates redesign Spotify's homepage and none who have worked under real product constraints.

When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."

Earning criteria — what you'll demonstrate

Understand IoT network topologies (star, mesh) and their trade-offs
Select and integrate sensors for environmental monitoring
Design a simple control algorithm for adaptive systems
Calculate energy savings from IoT-based optimization
Consider privacy and regulatory constraints in IoT deployments

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Aligned coursework coming soon.

Skills

Skills you'll demonstrate.

Each one shows up on your verified credential.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Career paths this builds toward

Canonical roles

IoT Data Scientist
A wind turbine, a hospital infusion pump, a fleet of delivery vans, each one streams telemetry that means nothing until someone extracts a signal from it. IoT data scientists work at that boundary between physical equipment and statistical inference. The role suits students who like the messiness of real-world sensors, missing readings, drift, and all, more than the tidiness of curated datasets. You'd grow into it by combining Python and streaming tools like Apache Kafka with enough hardware literacy to ask engineers the right questions. Anomaly detection becomes a recurring theme. Strong IoT data scientists translate between operations teams who want fewer breakdowns and business stakeholders who want fewer surprises, and that translation work is often where the real value shows up.
Data & Analytics

One more thing

You can put a credential on your CV by Friday.

Start this challenge