The Problem of Interoperability of Fusion Sensory Data from the Internet of Things

The Internet of Things (IoT) is a network of smart devices connected by the internet that allows communication, exchange of information, and interactive actions between devices or sensors such as smart home sensors and smart vehicles sensors. This chapter introduces the evolution of sensor fusion te...

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Bibliographic Details
Published in:Intelligent Multi-Modal Data Processing pp. 157 - 181
Main Authors: Eldin, Doaa Mohey, Hassanien, Aboul Ella, Hassanein, Ehab E.
Format: Book Chapter
Language:English
Published: United Kingdom John Wiley & Sons, Incorporated 2021
John Wiley & Sons, Ltd
Subjects:
fusion sensory data
Internet of Things
machine learning
multimodal data fusion
sensor fusion algorithms
ISBN:1119571383, 9781119571384
Online Access:Get full text
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Summary:The Internet of Things (IoT) is a network of smart devices connected by the internet that allows communication, exchange of information, and interactive actions between devices or sensors such as smart home sensors and smart vehicles sensors. This chapter introduces the evolution of sensor fusion techniques. Machine learning algorithms are considered the best way to help with big data for sensor fusion to improve accuracy and achieve high performance. They also can provide a solution for reducing the complexity of data fusion in IoT devices. The chapter presents a brief introduction to the IoT, outlines data fusion for IoT devices, and discusses the comparative study of data fusion techniques in the IoT. Multimodal data fusion in IoT can provide performance, expanded spatial coverage, increased confidence, minimized ambiguity, enhanced purpose detection, increased reliability, and greater dimensionality. The chapter compares IoT data fusion techniques, discusses the results, and proposes directions for future work.
ISBN:1119571383
9781119571384
DOI:10.1002/9781119571452.ch7