Data-Driven Waste Management

Eliminating the inefficiencies of static collection schedules. We use IoT sensors to provide real-time fill levels, optimizing labor and ensuring campus sanitation.

Real-Time Telemetry
Utilizing ESP32 microcontrollers and ultrasonic sensors to transmit fill-level data instantly via MQTT over WiFi.
Intelligent Analytics
A central dashboard aggregates data to identify overflowing bins and optimize collection routes.
Low Power Design
Embedded Deep-Sleep protocols ensure longevity, waking only on periodic timers or interrupt triggers.

© 2025 Concordia University - SOEN 422 Semester Project.