In the Internet of Things (IoT) era, people are very interested in wearable devices such as smart watches. These devices measure individual physiological time series such as blood pressure, heart rate, and EEG. With this functionality, people can check the status of their own health. This healthcare service usually sends individual physiological time series to remote clusters for calculation. A remote healthcare service is particularly necessary for patients suffering from chronic and urgent diseases such as cardiovascular disease. It is also necessary to predict urgent signals for proper treatment. One method to predict urgent signals is by clustering physiological time series and comparing the new physiological time series with the previous time series in a cluster. It means searching the time series similar to risk features. In other words, the detection and comparison of features in time series are important. Therefore, in this study, we propose a biosignal processing system based on the Haar transform of time series in IoT applications. We discuss the validity of this system according to various perspectives. The Haar transform of a time series reflects the trend of the time series; thus, we can recognize the trend of the time series more easily. In addition, we can reduce the storage size of the time series. This is especially helpful because the volume of a time series is massive in the IoT era. Although the reduction of information in a time series can distort the similarity accuracy, it does not distort it significantly.