Unsupervised Manifold Alignment for Wifi RSSI Indoor Localization

Khaliq, Zain; Mirdita, Paul; Refaey, Ahmed; Wang, Xianbin

doi:10.1109/ccece47787.2020.9255684

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Localisation [ 7 , 10 , 16 , 42 , 58 , 72 , 75 , 76 , 82 , 107 , 110 , 114 , 121 , 125 , 126 ]: Similar to position, localisation aims to provide better localisation accuracy by combining different techniques, technologies, and algorithms. In the current studies, localisation techniques have been used to locate people in different environments such as shopping malls, universities, hotels, among others.…”

Section: Resultsmentioning

confidence: 99%

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Quezada-Gaibor

Torres-Sospedra

Nurmi

et al. 2021

Sensors

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Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.

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Section: Resultsmentioning

confidence: 99%

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Quezada-Gaibor

Torres-Sospedra

Nurmi

et al. 2021

Sensors

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“…One of the major drawbacks of indoor localization systems is acquiring information on users' location through the central server [7]. This introduces the opportunities for unethical measures that may include tracking a client's location in real-time.…”

mentioning

confidence: 99%

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Mirdita

Bello

Refaey

et al. 2023

IEEE Open J. Commun. Soc.

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Advanced Indoor Positioning Systems (IPS) based on Received Signal Strength (RSS) fingerprints have been paramount in 6G network research and commercial exploitation due to their costeffectiveness and simplicity. Despite their popularity, the advent of 6G has prompted a shift towards exploring Deep Learning algorithms to further enhance their performance and precision. Deep Learning research typically demands large datasets, leading to reliance on data augmentation and crowdsourcing techniques for data collection. However, the traditional centralization of data in crowdsourcing poses privacy risks, and here is where Federated Learning (FL) comes into play. In light of this, our study introduces FL to bridge this divide in a decentralized way, eliminating the need for servers to acquire labeled data directly from users. This approach aims to minimize localization error in RSS fingerprints, preserve user privacy, and reduce system latency, all key goals for 6G networks. Moreover, we explore the use of power transmission techniques to further decrease the latency in the FL system. Our simulation outcomes confirm the superiority of FL over traditional Stochastic Gradient Descent (SGD) methods considering critical evaluation metrics like localization error and global loss, paving the way for efficient 6G implementation.

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“…• Localisation [61,55,37,92,46,88,10,5,81,106,101,11,76,44,107]: makes reference to recent improvements in relation to localisation.…”

Section: What Improvements Were Done In Similar Studies (Rq5)mentioning

confidence: 99%

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

Quezada-Gaibor¹

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Unsupervised Manifold Alignment for Wifi RSSI Indoor Localization

Cited by 4 publications

References 21 publications

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

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