Evaluation of time-series imaging for visual analytics and processing of an indoor air quality sensors network

The Internet of Things (IoT) systems provide the technological support to perform automatic indoor air quality monitoring. However, most of the data IoT systems needs processing to obtain valuable information. In this work, we analyze the suitability of imaging time-series in terms of the amount of information based on images entropy, the accuracy to detect abnormal indoor air quality conditions by supervised classification with artificial intelligence (AI) methods, and its computational cost.