# Missing Data Imputation in the Internet of Things Sensor Networks
## 1 Introduction  
The rapid increase in the use of IoT technology has resulted in a vast amount of data being collected, mostly from sensor devices. The IoT relies on data collected by various end devices, and data is collected instantly by physical objects that incorporate sensors and network connectivity [1; 2]. As a result, there has been a huge surge in the amount of data being generated and sent over the internet. When handling large amounts of data, it has become rather usual to come across large amounts of missing values in the data. Sensor data frequently contains missing values due to data collection and transmission errors [3]. Due to common-mode failures, data with missing values continues to emerge as a long-standing difficulty in the IoT architecture, potentially resulting in bias and loss of precision. These systems rely on data analytics applications that make decisions based on end device data. It is apparent that educating industrial users about the importance of owning intellectual property (IP) and identifying the merits of continuous development is the best way to increase their interest [4]. There has also been a constant threat of security breaches whichcould breach the ownership of IP, increasing the risk to established business models. This is especially true when considering IoT-enabled healthcare systems, and that is an excellent demonstration when designing distributed IT systems with comparable complicated needs and stakeholder requirements [4]. Atmospheric pollutants in urban areas are considered as a main factor that has led to the increase in respiratory sicknesses among citizens. Some of these pollutants, e.g., benzene have previously induced cancers when citizens are exposed for prolonged period of time [5]. Hence, it is important to accurately estimate the distribution of these pollutants as it is relevant for traffic management and assists in the design and mobilization of plans to tackle these problems. Currently, air pollution monitoring in urban areas is essentially carried out using networks of fixed stations that are spatially distributed. These stations can accurately and selectively estimate the concentration of various atmospheric pollutants using Low Cost Sensor (LCS) devices. However, it is sometimes challenging to adequately deploy these networks due to their size and cost [5; 6]. It has become possible to acquire spatio-temporal data variables for urban atmospheric pollutants by making use of LCS that are strategically placed in polluted areas. However, the data collected by these sensors are exposed to numerous issues such as drifts, bias and loss of data due to equipment failures [7]. The issue of missing data is a prevalent problem that affects most sensor network domains and other real-life datasets such as traffic [8], medical/health record systems, geo-informatics [9; 10], and industrial applications [11]. If missing data is not handled correctly, it can have a significant impact on the veracity of data-based insights. By minimising the sample size and thereby introducing bias, it may limit the study's final results [12]. Complete Case Analysis (CCA) and missing data imputation are two popular approaches for handling the issue of missing data in research. The goal of the CCA method is to discard instances of a given variable that has missing values. Although, this method is quite straight forward and easy to implement, it leads to loss of data which may hold some useful information. According to [13; 14], applying the CCA method will only be useful on a dataset with a large number samples and significantly low percentage of missing data. Imputation on the other hand involves filling in missing values with estimated values. There are two categories of missing data imputation: (i) single imputation and (ii) multiple imputation. The single imputation approach simply fills in missing values using a single and unique value, e.g., zero value, mean or mode of a given distribution.