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Review
. 2020 Oct 26;20(21):6076.
doi: 10.3390/s20216076.

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Rajalakshmi Krishnamurthi  1 , Adarsh Kumar  2 , Dhanalekshmi Gopinathan  1 , Anand Nayyar  3   4 , Basit Qureshi  5
Affiliations
Review

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Rajalakshmi Krishnamurthi et al. Sensors (Basel). .

Abstract

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. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. 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. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. 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 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. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Keywords: Internet of Things; data analysis; data fusion; data processing; emerging technologies.

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Conflict of interest statement

The authors of this manuscript have no Conflicts of Interest.

Figures

Figure 1
Figure 1
The basic architecture for IoT sensor data processing, data fusion and data analysis.
Figure 2
Figure 2
The workflow of the Temporal and Spatial Correlation Algorithm.
Figure 3
Figure 3
The workflow of the spatial–temporal model.
Figure 4
Figure 4
The workflow of the PMF model.
See this image and copyright information in PMC

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