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.

翻译：在复杂且动态的物联网环境中，对事件进行相关处理是一项具有挑战性的任务，这不仅因为需要处理的数据量庞大，还因为要求数据能够高效地实时处理。本文讨论了在实时或近实时事件管理中应执行的主要步骤，重点聚焦于事件检测与事件相关。我们研究了一种适用于实时事件检测的单变量变化检测算法，并基于一阶马尔可夫模型提出了一种逐步事件相关方案。所提出的理论应用于海事领域，并通过大量实验进行了验证，实验使用了源自部署于大型船舶船队中的大规模传感器网络的真实传感器数据流。