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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| Vydáno v: | Intelligent Multi-Modal Data Processing s. 157 - 181 |
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| Hlavní autoři: | , , |
| Médium: | Kapitola |
| Jazyk: | angličtina |
| Vydáno: |
United Kingdom
John Wiley & Sons, Incorporated
2021
John Wiley & Sons, Ltd |
| Témata: | |
| ISBN: | 1119571383, 9781119571384 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISBN: | 1119571383 9781119571384 |
| DOI: | 10.1002/9781119571452.ch7 |