CTLL: A Cell-Based Transfer Learning Method for Localization in Large Scale Wireless Sensor Networks

Tang, Zhanyong; Zhang, Jie; Gong, Xiaoqing; Xiao-hua, Cheng; Liu, Chen; Fang, Dingyi; Wang, Wei

doi:10.1155/2015/252653

Cited by 5 publications

(2 citation statements)

References 35 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [106], the authors propose a cell-based inductive TL (CTTL) method for a large-scale localization in a wireless sensor network. Specifically, as illustrated in Fig.…”

Section: B Outdoor Localizationmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

View full text Add to dashboard Cite

With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

show abstract

“…In [106], the authors propose a cell-based inductive TL (CTTL) method for a large-scale localization in a wireless sensor network. Specifically, as illustrated in Fig.…”

Section: B Outdoor Localizationmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to implement indoor localization services, researchers have studied various indoor positioning techniques using different technologies, including Wi-Fi, 1 Bluetooth Low Energy (BLE), 2 acoustic signals, 3–5 Channel State Information (CSI), 6,7 Ultra-Wideband (UWB), 8 Cell, 9 Camera, 10,11 Inertial Navigation Systems (INS), 12 and visible light. 13,14 Systems based on Wi-Fi and BLE signals do not achieve high accuracies due to the fluctuation of these signals.…”

Section: Introductionmentioning

confidence: 99%

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Shao

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Indoor magnetic field has attracted considerable attention in indoor location-based services, because of its pervasive and stable attributes. Generally, in order to harness the location features of the magnetic field, particle filters are introduced to simulate the possibilities of user locations. Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. However, the computation overheads of the magnetic matching are rather high, thus limiting their applications to mobile computing platforms and indoor location-based service providers that serve massive users. In order to reduce the computation overhead, the article presents a low-cost magnetic field fingerprint matching scheme. Based on the low-frequency features of the magnetic field, the scheme updates particle weights according to the mass center of the magnetic field deltas of pedestrian steps. The proposed low-cost scheme decreases the complexity of real-time fingerprints without harming the positioning performance. In order to further improve the positioning accuracy, not asking users to hold the smartphone in fixed attitudes, we also present a smartphone attitude detection method that enables the proposed scheme to automatically select proper fingerprints. Experiments convincingly reveal that the proposed scheme achieves about 1 m accuracy at 80% with low computation overheads.

show abstract

Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

Yadav,

Sharma

2023

Wireless Pers Commun

View full text Add to dashboard Cite

CTLL: A Cell-Based Transfer Learning Method for Localization in Large Scale Wireless Sensor Networks

Cited by 5 publications

References 35 publications

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

Contact Info

Product

Resources

About