MoLoc: Unsupervised Fingerprint Roaming for Device-Free Indoor Localization in a Mobile Ship Environment
| Autor: | Mozi Chen, Xuming Zeng, Cong Liu, Zheng Dong, Kezhong Liu, Jie Ma, Guangmo Tong |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science 010401 analytical chemistry Real-time computing Cruise Fingerprint (computing) 020206 networking & telecommunications 02 engineering and technology Fingerprint recognition 01 natural sciences Ship motions 0104 chemical sciences Computer Science Applications Data modeling Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Unsupervised learning Wireless Roaming business Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 7:11851-11862 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2020.3004240 |
| Popis: | Device-free indoor localization may play a critical role in improving passengers’ safety in large vessels, particularly for scenarios without equipped radios. However, due to dynamic internal and external influences from the sailing ship such as changing sailing speed, the existing localization systems suffer huge accuracy degradation in a mobile ship environment. The challenges are mainly due to rich and arbitrary ship motions and the resulting complicated impacts on the indoor wireless channels. To address the challenges, in this article, we first propose a ship motion descriptor to extract discriminative latent representation from complex ship motions by leveraging deep-learning techniques. Based on this representation, we then design a novel fingerprint roaming model, i.e., MoLoc, to automatically learn the predictive fingerprint variation pattern and transfer the online fingerprint measurement to adapt to dynamic ship motions in real time. Furthermore, an unsupervised learning strategy is proposed to train the fingerprint roaming model using unlabeled onboard collected data which do not incur any labor costs. We have implemented and extensively evaluated MoLoc on real-world cruise ships, where experimental results demonstrate that MoLoc improves localization accuracy from 63.2% to 92.8% compared to the state-of-the-art localization methods, including Pilot, LiFS, SpotFi, and AutoFi while achieving a mean error of 0.68 m. |
| Databáze: | OpenAIRE |
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