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CleanCity
AI Smart Bin Sensor based on LoRaWAN
LoRaWAN bin sensor that predicts fill times and optimizes collection routes for municipal waste management.
Fill Accuracy
±3%
Battery Life
3+ Years
CleanCity
Client
A waste management technology company providing smart city sensor solutions for optimizing municipal waste collection routes.
Challenge
Accurately measuring waste bin fill level through a 60mm diameter sensor hatch, rejecting false readings from rain, snow, and birds landing on the bin rim.
Product Concept & Architecture
The STM32WLE5 LoRa SoC integrates both the application MCU and LoRa transceiver. A VL53L3CX Time-of-Flight ranging sensor measures distance to waste surface.
Engineering Solution
The firmware implements a multi-modal rejection filter: ToF readings outside expected bin geometry are discarded. A 10-sample median filter smooths the signal. When fill level crosses 80%, the reporting interval shortens from 6 hours to 30 minutes. The TinyML model predicts 'bin full' time by learning historical fill patterns per bin location.
Result
±3% fill level accuracy. 3+ year battery life at 6-hour reporting. Route optimization reduced collection costs by 30%.
Deep Tech FAQ
What makes the AI Smart Bin Sensor based on LoRaWAN unique?+
The AI Smart Bin Sensor based on LoRaWAN leverages custom hardware and edge AI to deliver industry-leading performance and efficiency.
What are the core technical benefits?+
Key benefits include low power consumption, real-time processing, robust connectivity, and a compact form factor tailored for deployment.
Can this architecture be adapted for other use cases?+
Absolutely. The underlying methodology is modular and can be retargeted for various applications with minimal redesign.
Project Profile
Services Delivered
Hardware DesignEmbedded Software
Core Technologies
Wi-Fi/BluetoothTinyMLLoRaWAN
Silicon & Vendors
SemtechSTMicroelectronics
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