This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints. With this method, by checking control system behavior in detail using k-means clustering, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Due to these characteristics, the proposed k-means clustering realizes that anomaly prediction is performed by reducing memory consumption. Experiments were performed with actual data of control system for anomaly prediction. Experimental results show that the proposed anomaly prediction method can predict anomaly, and the proposed k-means clustering can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.

Computing and power resources are often limited in real-time embedded control systems. In this paper, we resolve the trade-off problem between control performance and power consumption in a real-time embedded control system with a dynamic voltage and frequency scaling (DVFS) uniprocessor implementing multiple control tasks. We formulate an optimization problem whose cost function depends on both the control performance and the power consumption. We introduce an adapter into the real-time embedded control system that adaptively assigns deadlines of jobs and clock frequencies according to the plant's stability and schedulability by solving the optimization problem. In numerical simulations, we show that the proposed adapter can reduce the power consumption while maintaining the control performance.