A Markov Model to Detect Sensor Failure in IoT Environments

The Internet of things is a rapidly expanding paradigm which is fundamentally altering the way in which we interact with technology. A range of new services are enabled by this technological revolution, one of which is the task of activity recognition from performing classification on sensed information, which is an area of research related to assisted living. The ability to reliably sense the environment is a crucial aspect of this application area, which has, to date, been prone to error and poor data quality. Therefore, it is essential that we are able to identify potentially anomalous data before it can have a serious effect in the application domain and evoke dangerous consequences. This work presents a novel Markov-based technique for detecting anomalous sensor events in constrained Internet of Things environments. Results from the experiment found a recall score of 97.9% and an F-measure of 92.0%, which represents a promising step forward in this research direction.