Data generated by your IoT sensors are a special case data source for
Anomaly Detection. This case is even more interesting because a fault
in the IoT infrastructure can be an anomaly itself.
For reference, check these white papers
Database techniques can be used to populate your data repository for
priming an anomaly detection algorithm, but only real-time generation
of precisely tailored data verifies that end-to-end processing works
MIMIC MQTT Simulator can simulate large numbers of heterogeneous
sensors generating desirable data patterns in real-time over MQTT. For
example, you can have miriads of sensors generating MQTT payloads
containing a "normal" pattern, and instruct a small subset of them to
"misbehave" predictably, then observe how long it take to detect this
By deterministically varying the anomaly patterns in the simulator you are
able to tune and regression test iterations in your detection algorithm.
You are able even to explore boundary conditions of the infrastructure
requirements, such as message rates, failure conditions, etc.