Evaluating missing value imputation strategies to enhance IoT data availability in the edge

The rapid implementation of Internet of Things (IoT) technologies in industrial and air quality monitoring applications has resulted in large amounts of data being acquired. In addition, the integration of the artificial intelligence of things (AIoT) enables decision-making at edge nodes, involving the execution of data-driven models close to the data source. Data loss has become a limiting factor in AI-based applications. One way to address missing data is to leverage data from co-existing sensors, which are potentially correlated, to impute missing values. This article evaluates a set of missing value imputation (MVI) techniques in an edge node environment, where constraints on data availability and computational complexity must be met. Specifically, models such as multiple imputation by chained equations (MICE), k-nearest neighbors (KNN), and models using variational autoencoders (VAE) are evaluated. A comprehensive evaluation is presented covering different scenarios of missing data in terms of the percentage of missing values, bursts of missing values, and the size of the data windows stored on edge nodes. This evaluation uses real sensor data from an air quality monitoring network and an industrial sensor network. The results show that the VAE is able to obtain good imputation performances (R2 greater than 0.90) while providing good uncertainty quantification (UQ) estimates with around 90%–95% of samples falling within the estimated confidence intervals. Moreover, a transfer learning-based VAE has been shown to adapt to the non-stationary nature of IoT sensing signals, and all methods have proven to be efficient in terms of execution time for the edge setting.