# Missing Data Imputation in the Internet of Things Sensor Networks
## 2 Motivation and Contribution  
The presence of missing values in datasets is a pervasive issue that has drawn attention from various research domains such as medical research [10], data compression [21] and sentence generation [22]. These collaborative efforts have led to the development of state-of-the-art machine learning techniques for solving this issue in datasets with unique characteristics. Our work follows the imputation paradigm presented in [23]. However, this study showcases various improvements to the work done by the previous authors. To further improve the accuracy of the final imputation result, we use stronger predictors and consider the covariates in our imputation model to generate and replace missing values with multiple imputed values rather than single imputed values in each group in order to preserve the variability of the data as opposed to the work in [23]. This study also improves on previous works by testing higher rates and different mechanisms of missing data and presents the efficiency of imputation techniques on machine learning tasks.  
In this study, we investigate the performance of various missing data imputation techniques for sensor networks in environmental monitoring stations. During data collection, sensor devices may fail or run into errors which results in the issue of missing data. Consequently, this will have an impact on the advanced analysis of the data collected by these sensor devices [24]. Traditional methods for handling this issue involves deleting instances of a dataset with missing values before proceeding to further analysis. This method is impractical because it totally disregards scenarios with missing values and fails to account for the complex distribution of environmental data, resulting in bias and imprecision [24]. It is possible to impute missing data by learning from the observed data and filling-in missing instances with single or multiple plausible values.  
The authors present a novel clustering based approach to missing value imputation for univariate missing data with varying missing patterns. This paper's main contributions are summarised below:  
* As various imputation techniques exist in the literature, when faced with real-life missing instances where there is no ground truth data, it is important to have imputation approaches that will embed the capability of selecting optimal algorithms for imputing missing values. We propose an imputation algorithm called BFMVI that is capable of choosing appropriate techniques for filling-in missing instances based on the nature and characteristics of the missing data.
* We also propose a reverse error score function RES(r) that is based on double Root Mean Squared Error (RMSE) calculations on two final imputation estimates to obtain the final imputation result for filling in missing instances.
* We experimentally demonstrate that considering highly correlated auxiliary variables in the imputation model will impute efficient predictors which will significantly improve the RMSE and MAE scores.
* Our proposed BFMVI algorithm shows a better performance as opposed to alternative benchmark techniques when missing values occur at different rates and consecutive periods of time.  
In order to exploit the merits of low latency, high energy efficiency, lower bandwidth consumption and improved data privacy, we suggest the application of the proposed method on the network edge. Bringing imputation algorithms to the edge makes it possible learn more about the dynamics of the urban systems and explore the potential of the generated data.