LoRa / LoRaWAN Integration with ASI Biont: AI-Driven Telemetry Without Code

Why LoRaWAN and AI Make a Killer Combo

LoRaWAN is the backbone of long-range, low-power IoT — perfect for agriculture, logistics, and smart cities. But raw sensor data is useless without intelligent analysis. ASI Biont connects to LoRa devices via MQTT (the most common bridge) or COM port through a Hardware Bridge, turning your LoRa network into an AI-controlled system. No dashboards, no custom apps — just chat.

How ASI Biont Connects to LoRa

ASI Biont doesn't connect directly to LoRa chips (it runs in the cloud). Instead, it talks to a LoRa gateway or bridge (like a RAK7249 or Heltec) via:
- MQTT: The gateway publishes sensor data to a broker (Mosquitto, HiveMQ). AI subscribes and analyzes.
- COM port: A USB-connected LoRa module (e.g., SX1276 on Arduino) is accessed via bridge.py on your PC, using serial_write_and_read() to send AT commands and read responses.

You simply tell the AI: “Connect to my RAK gateway at 192.168.1.100, MQTT topic lorawan/up, parse temperature and humidity.” AI writes the integration in seconds.

Real-World Use Cases

1. Agricultural Soil Monitoring

Setup: Heltec LoRa node + soil moisture sensor → RAK7249 gateway → MQTT → ASI Biont.
AI action: AI subscribes to sensor/soil, checks moisture levels. If below 30%, it sends a Telegram alert and logs data to PostgreSQL.

import paho.mqtt.client as mqtt

def on_message(client, userdata, msg):
    payload = msg.payload.decode()
    print(f"Received: {payload}")
    # AI parses payload and triggers alert if moisture < 30%

client = mqtt.Client()
client.on_message = on_message
client.connect("192.168.1.100", 1883, 60)
client.subscribe("sensor/soil")
client.loop_start()

AI runs this in execute_python (sandbox) — no loops, just a 30-second window to read and react.

2. Asset Tracking via GPS + LoRa

Setup: Dragino LGT-92 GPS tracker → LoRaWAN network → MQTT broker.
AI action: AI parses GPS coordinates from payload (e.g., Cayenne LPP format), tracks movement, and plots routes on a map using matplotlib.

import json
import paho.mqtt.client as mqtt
import matplotlib.pyplot as plt

locations = []
def on_message(client, userdata, msg):
    data = json.loads(msg.payload)
    lat = data['latitude']
    lon = data['longitude']
    locations.append((lat, lon))
    plt.plot([p[1] for p in locations], [p[0] for p in locations], 'bo-')
    plt.savefig('/tmp/route.png')

client = mqtt.Client()
client.on_message = on_message
client.connect("broker.emqx.io", 1883, 60)
client.subscribe("tracker/gps")
client.loop_start()

The AI saves the plot and sends it back to you in chat.

3. Warehouse Temperature Control

Setup: STM32 + SX1276 + DHT22 → COM port → Hardware Bridge.
AI action: AI sends serial_write_and_read(data="GET_TEMP\n") via industrial_command(). Device responds with TEMP:23.5 C. AI checks against threshold and can send back FAN_ON.

User: "Read temperature from COM3 at 115200 baud, if above 25°C turn on fan via relay."
AI: Uses industrial_command(protocol='serial://', command='serial_write_and_read', data='GET_TEMP\n', port='COM3', baud=115200)

4. Smart Parking Detection

Setup: LoRa magnetic sensor (e.g., Wzzard) → MQTT → ASI Biont.
AI action: AI analyzes occupancy patterns, predicts peak hours, and sends daily reports to email (via sendgrid).

5. Flood Monitoring

Setup: Water level sensor + LoRa node → TTN (The Things Network) → MQTT.
AI action: AI subscribes to water/level, applies moving average filter, triggers SMS alerts (via Twilio) if level exceeds 2m.

How to Start

1. Set up your LoRa gateway (RAK7249, Heltec, or any TTN-compatible). Ensure it publishes to an MQTT broker.
2. Prepare connection details: broker IP, topic, credentials.
3. Chat with ASI Biont: "Connect to my LoRa gateway at 192.168.1.100, topic lorawan/up, parse temperature and humidity, alert me on Telegram if temp > 40°C."
4. AI generates the Python script using paho-mqtt, runs it in execute_python, and starts monitoring.

No manual coding. No waiting for developers. Just describe — and the AI does the rest.

Why This Beats Traditional Dashboards

• Zero code: AI writes the integration in seconds.
• Flexible: Switch from MQTT to COM port in one sentence.
• Real-time alerts: Telegram, email, SMS — all through chat.
• Automatic logging: AI can save data to PostgreSQL or CSV.

Conclusion

LoRaWAN is powerful, but its true potential unlocks when combined with AI. ASI Biont transforms your LoRa network into an intelligent, self-managing system — no custom apps, no dashboards, just you and the AI. Try it today at asibiont.com.