# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 1 Introduction  
In the coming years of the Internet of Things (IoT), context-awareness bridges the interconnection between the physical world and virtual computing entities, and involves environment sensing, network communication, and data analysis methodologies [1]. Advancement enables several advanced IoT applications, such as intelligent healthcare systems, smart transport systems, smart energy systems and smart buildings. The IoT networks' unified architecture includes smart IoT-based application services and the underlying IoT sensor networks [2]. According to the Gartner forecast, the IoT global market envisions 5.8 billion IoT-based applications by 2020, with a 21% increase from 2019 [3]. Further, the IoT market's worldwide growth is propelled by wireless networking technologies and the adoption of emerging technologies such as cloud platforms. This trend leads to a drastic increase in demand for connected IoT devices and application services. The primary objectives of IoT sensor networks include (i) sensing the critical information from the external physical environment, (ii) the sampling of internal system signals, and (iii) obtainingmeaningful information from sensor data to perform decision-making [4; 5]. It is to be noted that IoT-enabled applications involve a wireless sensor network (WSN). Further, these wireless sensors are randomly positioned and capable of establishing an ad hoc network without infrastructure requirements. The wireless sensor network is reinforced by low-cost and lower power devices, such as Wi-Fi, Bluetooth, Zigbee, Near Frequency Communication, etc. However, these wireless-based networks incur difficulties, such as inference, loss of data, redundancy of data and different data generation [6; 7]. It can be observed that the raw sensor data from IoT sensors embed-large scale unclean and useless data. Thus, the raw sensor data need to undergo data cleaning processing, and then data analysis can be performed to obtain relevant information from this cleaned IoT sensor data [8; 9]. Further, the large quantity of unwanted and useless data can lead to high computation costs and the overutilization of resources in a constrained IoT sensor network. The most common data processing techniques are data denoising, data imputation, data outlier detection, and data aggregation [10]. It is observed that the raw sensor data exhibit unwanted changes and modifications in the original signal. This raw data signal left untreated leads to expensive resource utilization and computation requirement. As such, the raw sensor signal's data processing is essential, and a variety of existing solutions are addressed in this paper. In the IoT sensor network, the nodes are distributed, and several nodes are used to perform the same operation. Hence data integration or fusion from multiple sensors is required to improve accuracy in various IoT-based application services [11; 12; 13]. For example, in a real-time traffic monitoring system, data patching is useful for data fusion. The previous week's data are then fused to other data from the time of loss of data. This process involves count-and-classify data, loop-based data, vehicle speed measurement, automated number plate recognition (APNR), etc. As such, in this paper, the details of data fusion are also addressed. A further dimension of the IoT sensor network addresses the fact that the IoT sensors' data possess complex properties, such as voluminousness, veracity and velocity. Thus, it is essential to store this data for performing data analysis and achieve the desired outcome for IoT sensor-based applications. IoT sensor network integration with emerging technologies provides efficient methods to handle sensor data's dynamic and complex nature. Furthermore, machine learning and deep learning techniques provide a promising solution towards the analysis of IoT sensor data [14; 15; 16]. Incorporating these data analysis techniques results in deep insights into sensor data, and provides good knowledge related to hidden data patterns and further decision-making.