Robust Neighborhood Graphing for Semi-Supervised Indoor Localization With Light-Loaded Location Fingerprinting
The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawbac...
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Veröffentlicht in: | IEEE internet of things journal 2018-10, Vol.5 (5), p.3378-3387 |
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creator | Zhou, Mu Tang, Yunxia Tian, Zengshan Xie, Liangbo Nie, Wei |
description | The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely. |
doi_str_mv | 10.1109/JIOT.2017.2775199 |
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However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. 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subjects | Calibration Characteristic functions Construction costs Crowdsourcing Electronic devices Execution characteristic function (ECF) Fingerprinting Fingerprints Indoor environments indoor localization Internet of Things Linear programming Localization manifold alignment Manifolds Position (location) radio map semi-supervised learning Semisupervised learning Signal strength Smartphones Smoothness Wireless LAN Wireless networks |
title | Robust Neighborhood Graphing for Semi-Supervised Indoor Localization With Light-Loaded Location Fingerprinting |
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