ESOD: An Edge Streaming Data Outlier Detection Framework for IoT Platforms

Intelligent computing at the IoT edge allows tasks that are normally performed in the cloud to be performed at the IoT node, enabling low-latency applications and more efficient control and management of services. Among the tasks that can be performed on the IoT edge is improving data quality, such as outlier detection. This task is challenging because IoT nodes have fewer computational and storage resources than the cloud. In this paper, we propose an outlier detection framework adapted to IoT edge nodes that is lightweight, consumes few resources, adapts to changes in signal trends, and has the ability to provide real-time responses. The framework presented is based on the use of two windows. A sliding window for the current data, which captures the short-term changes in the signal, and a window that stores a summary of historical values, which captures whether the values are within the long-term range of the signal. We show how models using two windows reduce the number of false positives compared to models using only one window. A version for near real-time applications is also proposed, which improves detection by identifying trend changes in the signal at the cost of delaying the decision by a few samples.