Healing IoT Data for Indoor Air Quality Using Artificial Intelligence

This paper reviews the problems encountered with data sets recorded by IoT monitoring networks and proposes five data healing methods. The five proposed methods include classic approaches but also methods based on artificial intelligence algorithms for predicting values. Using a case study based on indoor air quality research, the performances of the five methods of filling the gaps in the recorded data are explored both from the point of view of the ease of evaluation and visualization of the data and from the point of view of the statistical properties of the series of values.