Building an IoT Monitoring Pipeline: MQTT to Anomaly Detection

Managing an IoT fleet with over 2,500 devices in Kenya isn't always straightforward, especially when you're dealing with intermittent connectivity and budget hardware. Recently, I had to set up a pipeline to catch anomalies in our data before they caused real headaches. Here's how I stitched together MQTT, Python, and some basic anomaly detection to get it done. The context Our devices are spread across rural areas with spotty internet, sending telemetry data every few minutes. This data includes temperature, humidity, and some custom sensor readings. Setting up a real-time monitoring system to alert us to anomalies, such as an unexpected spike in temperature, was challenging without exceeding our budget or falling apart due to connectivity issues. Enter MQTT: The messenger I chose MQTT for our messaging protocol because it’s lightweight, which is perfect for devices with limited resources. We’ve set up an MQTT broker on a local server that each device publishes to. The setup is straig