# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 5 Data Analysis
Further, the decision-making module enables periodic reports from nodes that serve as input values for a real-time analytical process, and computes the factors for improving the quality of service. _Edge-Based Data Analysis_: The authors in [83; 84; 85] series prediction, forecasting, and event-based data handling. For this purpose, many solutions, such as stochastic and regression models, can be performed at the edge level of the IoT sensor network. In this, the data analysis is carried out at the low-level sensor nodes. In the case of time series data forecasting, the two specific prediction models are carried out simultaneously in the sensor devices and the base station nodes. Based on the difference between the values predicted by some constant factor, the base station will be updated with new data. This ensures a low computation cost and resource utilization in an inefficient manner. The authors of [86] present trends in IoT-based solutions from the perspective of health-care. This research aims to provide efficient services within an Internet of Medical Things application, utilizing the edge computing paradigm. In this paper, the authors proposed a three tier IoT architecture that included edge devices, fog network and cloud servers. The sensors act as edge devices and are connected to fog networks for real time data analysis. For high-performance computation and increase storage capacity, these edge devices are connected to the cloud remote servers. The authors present an energy-efficient IoT data compression approach for edge-based machine learning. This research aims to reduce the cost of data transmission, therefore increasing the overall efficiency of data offloading in a cloud-based IoT network. The study applies a fast error-bounded lossy compressor on the collected data before transmission. Later, they apply supervised machine learning techniques to re-build the data. Their experimentation shows that the amount of data traffic is significantly reduced by a factor of 103. _Semantic Analysis of IoT Sensor Data_: The increasing amount of sensory data arises from making data and applications readily accessible and understandable to future users [87; 88]. The semantic enhancements structure and organize the data. This also allows interoperability between machines. The benefit of applying semantic technology to sensor data is the conceptualization and abstract interpretation of the raw data, making them computer-definable, and interlinking the data with existing data web resources. The role of semantics offers new methods for information processing and exploration. It also turns information into actionable knowledge. The most common approaches include (i) linked open data, (ii) real-time and linked stream processing, (iii) rule-based reasoning, (iv) machine learning-based approaches and (v) semantic-based reasoning. The semantic analysis method includes the IoT resources, services, and related processes described using semantic annotations. To add sense to the IoT data, various tools, including observation and measurement data, need to be connected. By applying significant inference and analysis mechanisms to the IoT data, one can also achieve data processing for different domains. This also allows access to domain information and related semantically enriched representations for other entities and/or existing data (on the web). Linked data is a process that connects to different resources and is currently being implemented on the Internet. The linked data approach allows for the interconnection of resources defined by other models and ontologies. The provision of automated resource tagging mechanisms using the principles available, such as linked data and the specification of automated mechanisms of association between various resources (e.g., place, style, provider, and other common properties), render IoT data accessible across different domains.