A Wifi Positioning Method Based on Stack Auto Encoder

Zhong, Yanru; Zeng, Yuan; Zhao, Shuaijie; Luo, Xiliang

doi:10.1109/icdh.2018.00057

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Due to the sheer uncertainty of the mobile phone's initial orientation and the accuracy of the inertial sensor, inertial navigation is not ideal. Yanru Zhong et al [77] developed a deep neural network using stacked auto encoders and performed pre-training to obtain a more accurate Wi-Fi indoor positioning model.…”

Section: Deep Autoencodermentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

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Section: Deep Autoencodermentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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A survey of deep learning approaches for WiFi-based indoor positioning

Feng

Nguyen

Luo

2021

Journal of Information and Telecommunication

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One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.

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Indoor Wi-Fi Fingerprint Localization Based on SDAE and MLP with Self-attention Mechanism

Zhang

2022

2022 China Automation Congress (CAC)

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A Wifi Positioning Method Based on Stack Auto Encoder

Cited by 3 publications

References 13 publications

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

A survey of deep learning approaches for WiFi-based indoor positioning

Indoor Wi-Fi Fingerprint Localization Based on SDAE and MLP with Self-attention Mechanism

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