In the area of agriculture, the IoT-based digital precision agriculture services are expected to reach US $4.5 billion worldwide by the year 2020 [12]. The ability of IoT to monitor in real-time and the relative ease of use has opened up, to researchers, a whole new range of using IoT in many applications. The compendium of in-situ sensors that are embedded within the IoT devices are the fundamental components that collect  
Figure 1: Abstraction of the Internet of Things (IoT) architecture. valuable raw data. The correct operation of the sensors within the IoT device plays a vital role in the overall performance of the system and the dependent processes, applications[13]. The IoT sensors are often deployed in harsh environments; guaranteeing the sensor's correct operation and predicting malfunctions, however, is quite difficult. Additionally, sensors within the IoT typically are the most inexpensive electronic components typically prone to malfunctions. A malfunctioning sensor produces corrupted data or erroneous readings or conflicting information to the IoT device [14]. When the IoT processes this corrupted sensor data, the overall performance of the IoT system is compromised, making it inaccurate and unreliable. The recently growing trend of automating many processes, like autonomous driving vehicles to reduce the number of crashes, emphasises the importance of the correct operation of sensors that are operating within the system. As the IoT systems work continuously, generating large volumes of multi-modal data, ensuring the accurate operation of the sensors within the IoT is critical and there should be a precise monitoring process in place to verify the behaviour and performance of the sensors within IoT. Additionally, this sensor monitoring process has to be automated, scalable and needs to be agile enough to be used for the streaming of raw data produced by the numerous sensors embedded within the IoT device. This monitoring process, commonly referred to as the sensor outlier detection, is known to detect any anomaly or deviation in the sensor's readings and is usually one of the key processes that influence the quality of the data collected by the sensor. Of late, within the research communities, the detection of outliers is of great interest [15; 16; 17; 18]. However, much of the current research on outlier detection is in the area of Wireless Sensor Networks (WSN) and is also used widely for fraud detection, network security breaches, target tracking, environmental and health monitoring. However, adequate research has not been done in identifying the sensor outliers in the context of IoTs. The unique characteristics of IoTs, when compared with WSN, shows that the traditional outlier detection techniques are not directly applicable to IoTs. Usually, an IoT is a compendium of multiple-similar sensors embedded as a unit capable of producing huge amounts of spatial-temporal data at low latency, unlike the WSN [19]. When there is erroneous data produced by one kind of sensing unit within the IoT caused by the failure or malfunctioning of one sensor out of a pair, identifying the faulty sensor to make the data provided by that sensor redundant in real-time, whilst allowing the data produced by the secondary similar intact sensor plays an essential role in the correct functioning of the IoT device.