Anomalous energy detection for resource-constrained embedded systems using tracing data analysis

Since the multi-sensor embedded systems are spread all over the urban and suburban areas, there should be a real-time performance analysis framework to monitor the energy consumption of sensors and services. To this end, applying artificial intelligence to model the behavior can play a key role in improving operational efficiency and over-consumption detection. Most of the energy over-consumption detection approaches make use of external devices such as smart plugs or designed for smart buildings rather than computing devices. Through this paper, a new architecture is proposed in which we extract real-time execution traces after identifying the suspicious running applications using the dynamic binary instrumentation technique. The output of this step is fed to a data analytic framework to detect any behavioral anomalies such as over-threshold energy consumption. Finally, depending on the decision made by the anomaly detection step, some sensors might be switched to the standby mode in order to meet the essential expectations for other sensors or services. Since we have a resource-constrained embedded system and a real-time decision-making constraint on sensor control, the amount of computation that could be done in real-time varies a lot. Accordingly, the desired performance may dictate how, when, and where the processing operations might occur (on the embedded system or on a remote server). This work is mainly focused on anomaly detection operations using tracing data analysis. The experimental results show that the over-consumption detection model achieves a high level of accuracy metrics for classifying abnormal consumption behavior.