# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 2 Basic Architecture  
In the technology era of the Internet of Things (IoT), the interconnection of physical things with virtual objects aims to enhance human life quality through advanced applications and growing sensor technology, communication networks and processing methodologies. A few examples of such advanced applications include connected smart city, intelligent transport systems, smart healthcare, smart building and smart grids. Towards enabling rapid advancement in IoT, the sensor networks play a vital role by sensing critical data from both the internal functional systems and the external environmental factors [17; 18]. The wireless-based sensor network is much more popular, as these networks can be deployed ad hoc without the prerequisite of any infrastructure. The wireless sensor network is capable of self-organizing and can be deployed randomly.  
Figure 1 depicts the basic architecture for IoT sensor data processing, fusion and analysis layers. The IoT sensor data layer primarily consists of various IoT sensors that can measure physical surroundings and capture real-time environment changes. The commonly used IoT sensors include temperature, pressure, humidity, level, accelerometer, gas, gyroscopes, motion sensors image, optical sensors, Radiofrequency Identifier (RFID) sensors, and Infra-Red (IR) sensors. The IoT sensors are mainly associated with the microprocessing unit, storage unit, control unit, power system and wireless communication interfaces. The IoT sensor devices are constrained in size, computing power, memory, networking capability and storage space. Wireless communication protocols, such as Wi-Fi, Zig Bee, Bluetooth, Near Frequency Communication (NFC) and LTE/4G mobile technologies, are commonly used for IoT sensor device communication.  
The majority of IoT sensor data incorporate real-time processing for industrial applications, healthcare, and scientific activities. For example, the healthcare body sensors to monitor the patients' critical conditions would generate massively voluminous data. These sensed data must be processed to remove uncertainties for further data analysis, so as to develop knowledge and decision-making. Thus,  
Figure 1: The basic architecture for IoT sensor data processing, data fusion and data analysis.  
the data processing layer targets different functions, such as data denoising, data outlier detection, missing data imputation and data aggregation.  
The data fusion layer is required to handle various sensor data challenges generated by several heterogeneous sensor devices. The data fusion data aim to integrate true sensor data from heterogeneous IoT sensor devices. The combined data from different sources are then passed to the data analysis layer for efficient knowledge generation and decision-making.  
The primary data fusion involves the direct fusion of data sensor data from different sensor devices. It incorporates initial feature extraction which is followed by data fusion. The enhanced method involves feature extraction followed by identity declaration and data fusion [19; 20]. This method allows for a high level of inferences of knowledge and much accurate decision-making. In recent years, the adoption of emerging technologies has revolutionized cloud computing, fog computing and edge computing towards IoT sensor data analysis. These enabling technologies provide a pervasive, reliable and convenient platform to handle IoT sensor data's dynamic, heterogeneous nature [21; 22]. As such, the data analytic layer aims at developing smart functionality to address a wide variety of IoT-based applications. The objectives of these platforms are to reduce the computation and storage cost, improve network transmission reliability, reduce the network delay, enhance IoT network security and privacy, ensure scalability, and allow failure- and risk-free IoT solutions.