Biblio

The increasing proliferation of the Internet of Things (IoT) devices and systems result in large amounts of highly heterogeneous data to be collected. Although at least some of the collected sensor data is often consumed by the real-time decision making and control of the IoT system, that is not the only use of such data. Invariably, the collected data is stored, perhaps in some filtered or downselected fashion, so that it can be used for a variety of lower-frequency operations. It is expected that in a smart city environment with numerous IoT deployments, the volume of such data can become enormous. Therefore, mechanisms for lossy data compression that provide a trade-off between compression ratio and data usefulness for offline statistical analysis becomes necessary. In this paper, we discuss several simple pattern mining based compression strategies for multi-attribute IoT data streams. For each method, we evaluate the compressibility of the method vs. the level of similarity between original and compressed time series in the context of the home energy management system.