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dc.contributor.authorBelay, Abebeen_US
dc.contributor.authorLin, Hsin-Piaoen_US
dc.contributor.authorWang, Li-Chunen_US
dc.date.accessioned2020-03-02T03:23:31Z-
dc.date.available2020-03-02T03:23:31Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn2327-4662en_US
dc.identifier.urihttp://dx.doi.org/10.1109/JIOT.2019.2948075en_US
dc.identifier.urihttp://hdl.handle.net/11536/153787-
dc.description.abstractWireless and mobile technologies, as well as their users, are growing rapidly as the Internet of Things (IoT) products, such as sensor-network technologies, mobile devices, and supporting applications, become widely dispersed. Owing to the dynamic changes in the wireless networks and the exponential growth of the IoT products, which make it difficult to locate large quantities of users and devices, providing accurate tracking and trajectory predictions in open and highly condensed wireless networks is extremely difficult. An adaptive and scalable system is required to offer accurate location-based services (LBSs) for the success of IoT. To enhance the attainment of IoT, we propose a hybrid of principal component analysis (PCA) and gated recurrent unit (GRU) algorithms for mobility predictions in a wireless urban area. During the system development processes, we first collect an LTE signal from three unmanned aerial vehicle base stations (UAV-BSs), the Wi-Fi signal strength from each reachable Wi-Fi access points (APs), and channel information from the Wi-Fi signal media. We then apply PCA to reduce the number of Wi-Fi features and to decrease signal noise. Next, we train the GRU algorithm to develop models that can predict the mobility of IoT device users. Finally, we evaluate the tracking and trajectory models. To evaluate the proposed techniques, we compare the common parameters of the GRU with those of other deep learning types. The proposed technique provides plausible and state-of-the-art results for mobility predictions of IoT devices in a wireless environment.en_US
dc.language.isoen_USen_US
dc.subjectInternet of Thingsen_US
dc.subjectWireless fidelityen_US
dc.subjectWireless sensor networksen_US
dc.subjectWireless networksen_US
dc.subjectTrajectoryen_US
dc.subjectPrincipal component analysisen_US
dc.subjectDeep learningen_US
dc.subjectgated recurrent units (GRUs)en_US
dc.subjectmobility predictionsen_US
dc.subjectprincipal component analysis (PCA)en_US
dc.titleMobility Predictions for IoT Devices Using Gated Recurrent Unit Networken_US
dc.typeArticleen_US
dc.identifier.doi10.1109/JIOT.2019.2948075en_US
dc.identifier.journalIEEE INTERNET OF THINGS JOURNALen_US
dc.citation.volume7en_US
dc.citation.issue1en_US
dc.citation.spage505en_US
dc.citation.epage517en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000508181000039en_US
dc.citation.woscount0en_US
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