Lost in data: recognizing type of time series sensor data using signal pattern classification

Abstract

AbstractWith the increase in number and size of Internet of Things systems, there is an ever-growing risk of (meta)data loss, as well as the maintenance overhead to mitigate such risks. The experts recognize three main challenges in this area that need to be tackled, namely (1) downsizing the manual work required for configuring sensor networks, (2) recovering metadata, such as sensor type, in case of connection issues, malfunctions or malicious actions in sensor networks, (3) rebuilding metadata lost due to unexpected problems within a data storage. Fortunately, all three challenges can be tackled with a uniform solution, namely the signal type classification approach, which is able to match raw signal to an appropriate data type. In this research, we evaluate and compare different approaches for signal type classification that can be used to recognize a signal type being read from an IoT sensor. This is done by using machine learning methods for modelling a signal represented as raw time series data. Three machine learning classification approaches are taken into a consideration, namely one class, two class and multi-class. According to the results of the evaluation, the most accurate multi-class random forest algorithm can correctly classify unknown signals in $$\sim {75}\%$$ ∼ 75 % of the cases based on only 20 consecutive sensor readings. Moreover, multi-class random forest can detect two most probable classes of monitored signal with the accuracy of 95%.