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dc.contributor.authorWang, Shie-Yuanen_US
dc.contributor.authorCheng, Yu-Hsiangen_US
dc.contributor.authorTarng, Jenn-Hwanen_US
dc.date.accessioned2020-10-05T02:02:22Z-
dc.date.available2020-10-05T02:02:22Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-2999-0en_US
dc.identifier.issn1530-1346en_US
dc.identifier.urihttp://hdl.handle.net/11536/155530-
dc.description.abstractSigfox is a new LPWAN (Low-Power Wide Area Networks) wireless technology proposed for IoT applications. Its transmit power is only 22.5 dBm, but its wireless transmission range can be up to 30 km in open space. In this paper, we propose a new machine learning-based localization method to predict the location of a Sigfox module. Our method divides the space around each base station into a few sectors and trains a more accurate path-loss-to-distance model for each sector to reflect its site specific multipath propagation environment. Our experimental results measured in a big city show that the maximum distance errors of our method are much smaller than those of the official Sigfox localization service in many cases.en_US
dc.language.isoen_USen_US
dc.titleImproving the Localization Accuracy for Sigfox Low-Power Wide Area Networksen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC)en_US
dc.citation.spage378en_US
dc.citation.epage383en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000568621700067en_US
dc.citation.woscount0en_US
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