Anomaly detection for NILM task with Apache Flink

Abstract

The topic of the 2020 DEBS Grand Challenge is to develop a solution for Non Intrusive Load Monitoring (NILM). Sensors continuously send voltage and current data into a stream processing application that would detect the pattern of power data based on the data characteristics. NILM is important in signal processing especially in those advancing areas such as 5G and IoT products, which generate massive amounts of data from the edge of the network. Our solution focuses on how to divide and parallelize jobs as small as possible while keeping some reasonable Service Level Agreement (SLA) including job sizes and latency so that it would be practical for edge or fog deployment. This paper describes our solution based on Apache Flink, a stream processing framework, and the DBSCAN density based clustering algorithm for anomaly detection through the context of data provided by DEBS Grand Challenge.