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dc.contributor.authorQiu, Jun-Weien_US
dc.contributor.authorTseng, Yu-Cheeen_US
dc.date.accessioned2017-04-21T06:56:31Z-
dc.date.available2017-04-21T06:56:31Z-
dc.date.issued2016-07-15en_US
dc.identifier.issn1530-437Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/JSEN.2016.2566679en_US
dc.identifier.urihttp://hdl.handle.net/11536/133878-
dc.description.abstractMuch research has addressed indoor localization by integrating portable/wearable sensing and communication technologies. While GPS has dominated the outdoor environments, indoor localization schemes have to consider different observations, such as radio-frequency signals, vision or motions data, and apply data fusion to combine various sensor inputs. In this paper, we observe that when two devices meet up, which we call machine-to-machine (M2M) encountering, they can collaboratively calibrate each other\'s potential locations via M2M communications. We apply this technique to particle filter (PF), a common fusion technique, and show how to take the M2M encountering opportunities, which may happen frequently in crowded areas, to allow user devices to collaboratively calibrate their locations. Hence, the PF technique, which normally fuses observations from individual devices, is extended to an inter-PF, cross-device domain. We validate our inter-PF solution by simulations as well as a prototype system with smartphones and ZigBee infrastructure deployed in an office building. It is verified that the inter-PF helps converge the positioning results more rapidly, and improves location quality. Also, the proposed encountering mechanism has potential to be applied to other localization algorithms to improve their accuracy.en_US
dc.language.isoen_USen_US
dc.subjectCollaborative localizationen_US
dc.subjectdead reckoningen_US
dc.subjectmachine-to-machine (M2M) communicationen_US
dc.subjectparticle filteren_US
dc.subjectwireless positioningen_US
dc.titleM2M Encountering: Collaborative Localization via Instant Inter-Particle Filter Data Fusionen_US
dc.identifier.doi10.1109/JSEN.2016.2566679en_US
dc.identifier.journalIEEE SENSORS JOURNALen_US
dc.citation.volume16en_US
dc.citation.issue14en_US
dc.citation.spage5715en_US
dc.citation.epage5724en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000379601600028en_US
Appears in Collections:Articles