Towards Autonomous Anomaly Management Using Semantic Technologies at the Edge

Abstract

We present an approach that autonomously adapts sensor monitoring of an IoT environment. Based on semantic technologies, our solution supports the generation of relevant continuous queries when certain anomalies are identified. The generation consists of a query graph extension which is triggered when some rules are fired. These queries are executed on a graph database system designed for Edge computing. We evaluate the accuracy of the generated queries, the robustness, and the latency of our system in a real use case consisting of a smart building context equipped with multiple sensors.