Stepwise correlation of multivariate IoT event data based on first-order Markov chains

Correlating events in complex and dynamic IoT environments is a challenging task not only because of the amount of available data that needs to be processed but also due to the call for time efficient data processing. In this paper, we discuss the major steps that should be performed in real- or near real-time event management focusing on event detection and event correlation. We investigate the adoption of a univariate change detection algorithm for real-time event detection and we propose a stepwise event correlation scheme based on a first-order Markov model. The proposed theory is applied on the maritime domain and is validated through extensive experimentation with real sensor streams originating from large-scale sensor networks deployed in a maritime fleet of ships.