Semi-Self Representation Learning for Crowdsourced WiFi Trajectories
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915229096476672 |
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| author | Kuo, Yu-Lin Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann |
| author_facet | Kuo, Yu-Lin Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann |
| contents | WiFi fingerprint-based localization has been studied intensively. Point-based solutions rely on position annotations of WiFi fingerprints. Trajectory-based solutions, however, require end-position annotations of WiFi trajectories, where a WiFi trajectory is a multivariate time series of signal features. A trajectory dataset is much larger than a pointwise dataset as the number of potential trajectories in a field may grow exponentially with respect to the size of the field. This work presents a semi-self representation learning solution, where a large dataset $C$ of crowdsourced unlabeled WiFi trajectories can be automatically labeled by a much smaller dataset $\tilde C$ of labeled WiFi trajectories. The size of $\tilde C$ only needs to be proportional to the size of the physical field, while the unlabeled $C$ could be much larger. This is made possible through a novel ``cut-and-flip'' augmentation scheme based on the meet-in-the-middle paradigm. A two-stage learning consisting of trajectory embedding followed by endpoint embedding is proposed for the unlabeled $C$. Then the learned representations are labeled by $\tilde C$ and connected to a neural-based localization network. The result, while delivering promising accuracy, significantly relieves the burden of human annotations for trajectory-based localization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semi-Self Representation Learning for Crowdsourced WiFi Trajectories Kuo, Yu-Lin Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann Machine Learning Computer Vision and Pattern Recognition WiFi fingerprint-based localization has been studied intensively. Point-based solutions rely on position annotations of WiFi fingerprints. Trajectory-based solutions, however, require end-position annotations of WiFi trajectories, where a WiFi trajectory is a multivariate time series of signal features. A trajectory dataset is much larger than a pointwise dataset as the number of potential trajectories in a field may grow exponentially with respect to the size of the field. This work presents a semi-self representation learning solution, where a large dataset $C$ of crowdsourced unlabeled WiFi trajectories can be automatically labeled by a much smaller dataset $\tilde C$ of labeled WiFi trajectories. The size of $\tilde C$ only needs to be proportional to the size of the physical field, while the unlabeled $C$ could be much larger. This is made possible through a novel ``cut-and-flip'' augmentation scheme based on the meet-in-the-middle paradigm. A two-stage learning consisting of trajectory embedding followed by endpoint embedding is proposed for the unlabeled $C$. Then the learned representations are labeled by $\tilde C$ and connected to a neural-based localization network. The result, while delivering promising accuracy, significantly relieves the burden of human annotations for trajectory-based localization. |
| title | Semi-Self Representation Learning for Crowdsourced WiFi Trajectories |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.03756 |