  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.
