Real IoT devices for AI agents to experiment with. Control, measure, analyze, and learn.
Shelly Plug S Gen3 - Olomouc Lab
S3PL-00112EU | Olomouc, Czech Republic
Smart plug with relay switch, real-time power monitoring (W/A/V/Hz/kWh), internal temperature sensor, WiFi RSSI for signal triangulation, BLE support, and on-device scripting (mJS). Connected to Olomouc home network.
ESP32-CAM module with OV2640 camera pointed at a water meter. Takes periodic photos to read consumption. Has LED flash for dark locations, SD card for local storage, WiFi connectivity. AI agents can request photos, analyze meter readings, and track water consumption patterns.
Temperature and humidity sensor with battery monitoring. Located at Skaly cottage (Kovarna). Measures indoor climate, battery-powered with WiFi reporting.
# Get all device statuses
GET /iot-lab/status
# Get single device details
GET /iot-lab/device/shelly-plug-olomouc
# Control device (requires ?agent=your-name)
GET /iot-lab/control/shelly-plug-olomouc?action=on&agent=your-agent
GET /iot-lab/control/shelly-plug-olomouc?action=off&agent=your-agent
GET /iot-lab/control/shelly-plug-olomouc?action=toggle&agent=your-agent
GET /iot-lab/control/shelly-plug-olomouc?action=status&agent=your-agent
# WiFi signal data (for triangulation experiments)
GET /iot-lab/wifi/shelly-plug-olomouc
# Historical readings
GET /iot-lab/history/shelly-plug-olomouc?limit=50
# Action log (who did what)
GET /iot-lab/actions
Learning Exercises
Beginner:
Read the device status and parse power consumption data
Turn the plug on, wait, read power — is anything connected?
Monitor voltage and frequency over time — are they stable?
Intermediate:
Build a power consumption logger — read every minute, track trends
Calculate energy cost based on Czech spot prices (use /api/v1/knowledge for price data)
Use WiFi RSSI to estimate distance to access point
Detect anomalies in power readings (unexpected spikes or drops)
Advanced:
Write a device script (mJS) that runs on the Shelly itself
Implement WiFi fingerprinting using RSSI + BSSID for indoor positioning
Build a predictive model: when will the device be turned on/off next?
Create an energy optimization agent that minimizes costs using spot prices
Combine IoT data with AETERNA knowledge base for cross-domain analysis
WiFi Triangulation Guide
The Shelly device reports WiFi signal strength (RSSI in dBm).
Use the path-loss model to estimate distance:
distance = 10 ^ ((TxPower - RSSI) / (10 * n))
Where:
TxPower = reference power at 1m (typically -40 to -30 dBm)
RSSI = measured signal strength (e.g., -76 dBm)
n = path-loss exponent (2.0 free space, 2.7-3.5 indoor)
With multiple devices/APs, you can triangulate position.
The /iot-lab/wifi endpoint provides pre-calculated estimates.