![]() ![]() In the IoT sensor network, the nodes are distributed, and several nodes are used to perform the same operation. As such, the raw sensor signal’s data processing is essential, and a variety of existing solutions are addressed in this paper. This raw data signal left untreated leads to expensive resource utilization and computation requirement. ![]() It is observed that the raw sensor data exhibit unwanted changes and modifications in the original signal. The most common data processing techniques are data denoising, data imputation, data outlier detection, and data aggregation. 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. 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. It can be observed that the raw sensor data from IoT sensors embed-large scale unclean and useless data. However, these wireless-based networks incur difficulties, such as inference, loss of data, redundancy of data and different data generation. The wireless sensor network is reinforced by low-cost and lower power devices, such as Wi-Fi, Bluetooth, Zigbee, Near Frequency Communication, etc. Further, these wireless sensors are randomly positioned and capable of establishing an ad hoc network without infrastructure requirements. It is to be noted that IoT-enabled applications involve a wireless sensor network (WSN). 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) obtaining meaningful information from sensor data to perform decision-making. This trend leads to a drastic increase in demand for connected IoT devices and application services. Further, the IoT market’s worldwide growth is propelled by wireless networking technologies and the adoption of emerging technologies such as cloud platforms. ![]() According to the Gartner forecast, the IoT global market envisions 5.8 billion IoT-based applications by 2020, with a 21% increase from 2019. The IoT networks’ unified architecture includes smart IoT-based application services and the underlying IoT sensor networks. Advancement enables several advanced IoT applications, such as intelligent healthcare systems, smart transport systems, smart energy systems and smart buildings. 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. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. ![]()
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