## 1 Introduction  
The Internet of Things (IoT) is a marvelous integration of several technologies that enable interconnectivity among everyday objects and devices, allowing data and intelligence sharing among them. In recent times, the applications of the Internet of Things (IoT) have expanded rapidly in diversified domains. The IoT is providing more flexible and automated patient monitoring in critical scenarios [1; 2]. In agriculture, IoT-enabled systems can perform efficient irrigation [3], plant disease detection and control [4], and monitoring of soil and environment states [5; 6]. In power systems, the IoT can ensure reliable operation of smart grids [7], analysis of electricity demand [8], and anomaly detection in grids [9]. IoT systems can bring us a smarter environment for living by automating lighting systems, waste disposal and management; parking allocation utilizing electric vehicles; and home automation [10; 11]. At its core, there are several underlying components, such as sensors, micro-controllers and embedded systems, wireless networking, data analytics, and edge computing, that bring the IoT into its functional existence. Among the core elements, sensors and sensor networks play a pivotal role because they are the means to interact with the physical world. The majority of IoT applications rely on data-driven decision-making, analysis, and prediction based on the data collected by sensors. Thus, the reliability of sensor data is of paramount importance in IoT applications. Depending on the specific application,sensors can be deployed in rugged environments like industries or in open environments with extreme weather conditions [12]. Aside from the environmental conditions, a sensor device can deviate from its normal functionality because of prolonged operation, hardware malfunctions, and improper installation. A sensor is considered faulty when the data stream generated demonstrates a significant deviation from its specified range. Such deviations are deemed anomalous or faulty [13]. In a broad context, sensors can be affected by two types of fault: incipient and abrupt [14]. Incipient faults are gradual in nature, develop slowly over time, and are hard to detect immediately. On the other hand, abrupt faults appear suddenly in sensor readings and are marked by a rapid and sometimes significant deviation from the normal readings. An elaborate classification is provided in Figure 1.  
In the current data-driven world, sensor fault detection and classification tasks carry great significance. In critical systems like those in the automotive and aerospace industries, undetected faulty sensors can compromise the safety and reliability of a system and could lead to catastrophic accidents. The timely detection of a sensor fault can prevent unwanted downtime and unnecessary maintenance, and ensuring sensor data integrity helps make data-based control and decision-making more accurate. [PERSON] and [PERSON] [15] classified sensor fault analysis into three broad categories: knowledge-based, model-based, and data-driven approaches. Among these approaches, data-driven techniques are now widely explored by the research community due to the availability of unprecedented amounts of data generated from the integration of the IoT. The improvements in sensor technology, networking, and efficient computation power have given the data-driven sensor fault the upper hand over the other approaches. Several popular machine learning algorithms are utilized in sensor fault classification and analysis tasks. The support vector machine (SVM) [16; 17; 18], K-nearest neighbor (KNN), random forest (RF)-based classifiers, Gaussian Naive Bayes (GNB), and Artificial Neural Networks (ANNs) have demonstrated compelling results in sensor fault classification [19; 20; 21]. Several deep convolutional neural network (CNN) architectures are also employed for this purpose.