Deep Learning-Based Device-Free Localization Using ZigBee

Sun, Yongliang; Wang, Xiaocheng; Zhang, Xuzhao

doi:10.1007/978-981-13-9409-6_247

Cited by 1 publication

(2 citation statements)

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“…Sun et al investigated device-free indoor ZigBee localization and proposed a deep learning convolutional neural network (CNN) model [ 31 ]. Yang and Wu used deep neural networks and wireless radio links in the IoT to build a network based on the ZigBee protocol that could be used for single target localization in another study of device-free localization [ 32 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian

Macrina

Scalzo

et al. 2023

Sensors

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The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

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Section: Literature Reviewmentioning

confidence: 99%

“… Classification of the reviewed literature, with emphasis on the learning algorithms used [ 28 , 29 , 31 , 32 , 33 , 34 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. …”

Section: Figurementioning

confidence: 99%