Monitoring

The main task of the Monitoring component in IoTCrawler is to monitor the connected data streams in order to detect faulty/anomalous data samples.

The subcomponent Fault Detection is responsible to detect ‘unusual behaviour’ in the data streams and determine if a fault is present. In this case, counter measures are triggered, including recovery mechanisms to provide a quick response by imputing single StreamObservations or deploying a Virtual Sensor. To detect faults in a single data stream the Fault Detecttion uses a modified Dirichlet Process Gaussian State Machine Model and a ARIMA-based approach. The Fault Recovery utilises Markov Chain Monte Carlo (MCMC) methods to generate sensor samples.

In case a faulty data stream is detected, the subcomponent Virtual Sensor Creator is able to deploy a Virtual Sensor as a counter measure. It uses machine learning techniques to create sensor samples in relation to correlating, neighbouring sensors.

The following figure shows an overview of the Monitoring component and the interactions with other IoTCrawler components. For more details see the respective subsections.

The documentation for the Monitoring’s subcomponents can be found here:

• Fault Detection
• Fault Recovery
• Virtual Sensor Creator