![]() Missing values on the other hand, are mostly imputed using Association Rule Mining. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. ![]() The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. ![]() This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. ![]() ![]() Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. ![]()
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