Event Correlation and Forecasting over Multivariate Streaming Sensor Data

Abstract

Event management in sensor networks is a multidisciplinary field involving several steps across the processing chain. In this paper, we discuss the major steps that should be performed in real- or near real-time event handling including event detection, correlation, prediction and filtering. First, we discuss existing univariate and multivariate change detection schemes for the online event detection over sensor data. Next, we propose an online event correlation scheme that intends to unveil the internal dynamics that govern the operation of a system and are responsible for the generation of various types of events. We show that representation of event dependencies can be accommodated within a probabilistic temporal knowledge representation framework that allows the formulation of rules. We also address the important issue of identifying outdated dependencies among events by setting up a time-dependent framework for filtering the extracted rules over time. 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 ships.