Edge-Based Missing Value Imputation For IoT Platforms: Towards Artificial Intelligence of Things

Recently, the use of Internet of Things (IoT) technologies has been widely adopted, enabling large-scale monitoring and data acquisition. Together with the so-called artificial intelligence of things (AIoT), data-driven models are to be executed close to the data source. Nevertheless, one potential problem is the quality of the data used to feed these models. Data loss is a common issue in these systems and can hinder the use of monitoring data and the application of artificial intelligence (AI) models. This paper evaluates a set of missing value imputation techniques, emphasizing their application in an edge setting. Specifically, offline and sliding window variants are evaluated to meet data availability and computational complexity criteria. The benchmarking has been performed using the data of a real IoT air quality monitoring platform. The results demonstrate that the windowed variants can approach the offline performance using reduced sliding windows of 25 to 100 samples per sensor.