Federated Learning Sensor Node for Smart City Kit with ESP32 + Sensor
Build a Federated Learning Smart City Sensor Node with ESP32 Edge AI
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
As cities deploy thousands of sensors, sending raw data to the cloud is costly and invasive. This kit lets you build an edge AI node that trains a local anomaly detection model on air quality, temperature, and CO2 readings, then shares only model updates—not raw data—via MQTT. It’s a real-world implementation of federated learning for smart cities, right on your workbench.
What You'll Build
A weatherproof sensor node that continuously monitors PM2.5, CO2, temperature, and humidity, trains a TinyML anomaly model on-device, and pushes encrypted weight deltas to a LoRa/MQTT gateway. The node runs on solar-recharged 18650 cells and logs all sensor data and model metadata to a microSD card for offline analysis. The waterproof enclosure and power management make it ready for field deployment in street-level cabinets or rooftops.
What You'll Learn
- Implement on-device training of anomaly detection models using TensorFlow Lite Micro on ESP32-S3
- Interface PMS5003, MH-Z19B, and DHT22 sensors over UART, I2C, and one-wire protocols with unified firmware
- Transmit weight deltas via LoRa and MQTT to a central gateway, preserving data privacy
- Design a solar-powered system with battery management, power regulation, and deep-sleep firmware for multi-day autonomy
Kit Contents
| Component | Quantity |
|---|---|
| ESP32-S3 Dev Board | 1 |
| PMS5003 Particle Sensor | 1 |
| MH-Z19B CO2 | 1 |
| DHT22 | 1 |
| DS3231 RTC | 1 |
| MicroSD Module | 1 |
| RA-02 LoRa | 1 |
| 433MHz Antenna | 1 |
| LM2596 Buck Converter | 1 |
| Solar Panel 6V 2W | 1 |
| 18650 Cell | 2 |
| TP4056 Module | 1 |
| 100nF Caps | 15 |
| PCB Prototype Board | 3 |
| Waterproof Enclosure | 1 |
| Soldering Iron | 1 |
| Solder Wire | 1 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part works with TinyML frameworks and sensor protocols | Pre-tested ESP32-S3 firmware with unified sensor drivers and I2C/UART multiplexing |
| Build support | Forums and scattered tutorials | AI companion trained on this exact ML pipeline, sensor wiring, and model quantization |
| Time to first working build | Days of debugging sensor wiring and model quantization | Hours, with a step-by-step checklist and a pre-trained model template |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This kit is designed for final-year B.Tech and M.Tech students in ECE, CS, or AI who are building edge AI capstone projects; for researchers prototyping privacy-preserving sensor networks in smart city initiatives; and for IoT professionals at startups who need a ready-to-deploy node. It fits the advanced track of Smart India Hackathon, BITS/IIT/NIT/VIT major projects, and industry R&D labs exploring federated learning on resource-constrained devices.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
The included AI companion walks you through each step with wiring diagrams and code snippets. For edge cases, our WhatsApp support responds within 24 hours, often within a few hours during business days.
Do I need prior experience with federated learning?
No. The kit’s companion includes a conceptual primer and a pre-trained model that you can deploy immediately. The build is structured to teach the full pipeline from data collection to encrypted weight updates, so you gain hands-on understanding even without a deep ML background.
Can this node operate completely off-grid?
Yes. The 6V 2W solar panel charges the dual 18650 cells through the TP4056, and the LM2596 buck converter provides stable 3.3V and 5V rails. Deep-sleep firmware and efficient sensor polling stretch runtime to over 48 hours without sun, making it suitable for remote or rooftop deployments.
How does the node communicate with a central server?
The RA-02 LoRa module transmits weight deltas to a local gateway that bridges to MQTT over Wi-Fi or LTE. The companion shows you how to configure the LoRa parameters and set up an MQTT broker, ensuring your node can join a federated learning cluster even without direct internet access.
ESP32-S3 trains local anomaly model on PM2.5 + temp + CO2 data. Pushes weight deltas to gateway via MQTT.
What's in this kit
- ESP32-S3 Dev Board
- PMS5003 Particle Sensor
- MH-Z19B CO2
- DHT22
- DS3231 RTC
- MicroSD Module
- RA-02 LoRa
- 433MHz Antenna
- LM2596 Buck Converter
- Solar Panel 6V 2W
- 18650 Cell x2
- TP4056 Module
- 100nF Caps x15
- PCB Prototype Board x3
- Waterproof Enclosure
- Soldering Iron
- Solder Wire
Choose your assembly option:
- Soldering Kit — 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
- Breadboard Combo — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items