Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Ho, Ivan (EIE) | en_US |
| dc.creator | Guo, Jingtao | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12065 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | FedPos : a federated transfer learning framework for CSI-based Wi-Fi indoor positioning | en_US |
| dcterms.abstract | Fingerprinting-based Wi-Fi indoor localization, as a technology for determining the location of a target device based on wireless measurements, is gaining popularity as a result of its diverse uses, high accuracy, and the extensive deployment of Wi-Fi infrastructure. This dissertation proposes FedPos, a federated transfer learning framework together with a novel position estimation method for fingerprinting-based Wi-Fi indoor positioning. Compared with traditional machine learning with privacy leakage problems and the cloud model trained through federated learning (FL) fails in personalization, the FedPos framework aggregates non-classification layer parameters of models trained from different environments to build a robust and versatile encoder on the cloud server while preserving user privacy. The global cloud encoder can aggregate different classifiers and then construct personalized models for new users through fine-tuning. The proposed framework can be updated incrementally and is highly extensible. Specifically, we exploit channel state information (CSI) as the positioning feature and assess the transferability of a lightweight convolution neural network (CNN) model in unfamiliar environments. We evaluate the performance of our proposed framework and position estimation method in different indoor environments. The experimental results show that our proposed framework can achieve a mean localization error of 42.183 cm in a 64-position living room. They also confirm that FedPos can achieve a 5.23% average localization performance boost and reduce the average model training time by about 34.78% when compared with normal training. By reusing part of the feature extractor layers that are trained from other environments, at least 65% of training data can be saved to achieve a localization performance that is similar to the base model. Overall, the proposed position estimation method can effectively improve the localization accuracy as compared with three other existing CSI-based methods. | en_US |
| dcterms.extent | vii, 48 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Indoor positioning systems (Wireless localization) | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6522.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.99 MB | Adobe PDF | View/Open |
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