Object oriented time series exploration: Applied to power consumption analysis of embedded systems

Abstract

Performance monitoring and anomaly detection are major issues in designing and maintaining electronic devices and systems. In recent years, they become more difficult due to the increased complexity of hardware and software. Hence, an important point is to collect representative signal samples and reveal characteristic features allowing to evaluate device operational profiles. This results in the need of an efficient time series analysis. This problem is considered in relevance to embedded systems and Internet of Things devices. The paper presents a new scheme of decomposing time series by introducing higher level objects targeted at the searched system properties. They create a compact state model which facilitates deriving knowledge on system behaviour to validate correctness of its operation. The collected samples are aggregated into objects according to predefined similarity metrics, these objects can be traced, correlated, and merged with relevant operational log events. For this purpose, a set of original algorithms have been composed and included in the developed software tool. The presented approach has been evaluated on a representative dataset obtained from commercial Holter devices and was used to explore their energy consumption efficiency.