{"id":"https://openalex.org/W2919031495","doi":"https://doi.org/10.1109/glocomw.2018.8644270","title":"Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements","display_name":"Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2919031495","doi":"https://doi.org/10.1109/glocomw.2018.8644270","mag":"2919031495"},"language":"en","primary_location":{"id":"doi:10.1109/glocomw.2018.8644270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocomw.2018.8644270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1983/4cb178e0-b7be-4940-bea0-9afc43fa3859","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088663438","display_name":"Cam Ly Nguyen","orcid":"https://orcid.org/0000-0002-6823-4386"},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Cam Ly Nguyen","raw_affiliation_strings":["Toshiba Corporation, Corporate R&D Center, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toshiba Corporation, Corporate R&D Center, Japan","institution_ids":["https://openalex.org/I1292669757"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046029646","display_name":"Orestis Georgiou","orcid":"https://orcid.org/0000-0003-2303-6754"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Orestis Georgiou","raw_affiliation_strings":["School of Mathematics, University of Bristol, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, University of Bristol, UK","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038811120","display_name":"Vorapong Suppakitpaisarn","orcid":"https://orcid.org/0000-0002-7020-395X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Vorapong Suppakitpaisarn","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5582,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.83395373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rss","display_name":"RSS","score":0.9843907356262207},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7048563957214355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5565172433853149},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4828236997127533},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.473768025636673},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4727431535720825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42043250799179077},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3798114061355591},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3250793218612671}],"concepts":[{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.9843907356262207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7048563957214355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5565172433853149},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4828236997127533},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.473768025636673},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4727431535720825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42043250799179077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3798114061355591},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3250793218612671},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/glocomw.2018.8644270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocomw.2018.8644270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire/4cb178e0-b7be-4940-bea0-9afc43fa3859","is_oa":true,"landing_page_url":"https://hdl.handle.net/1983/4cb178e0-b7be-4940-bea0-9afc43fa3859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nguyen, C L, Georgiou, O & Suppakitpaisarn, V 2019, Improved Localization Accuracy Using Machine Learning : Predicting and Refining RSS Measurements. in 2018 IEEE Globecom Workshops, GC Wkshps 2018 : Proceedings of a meeting held 9-13 December 2018, Abu Dhabi, United Arab Emirates.., 8644270, Institute of Electrical and Electronics Engineers (IEEE), pp. 1506-1512. https://doi.org/10.1109/GLOCOMW.2018.8644270","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/4cb178e0-b7be-4940-bea0-9afc43fa3859","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/4cb178e0-b7be-4940-bea0-9afc43fa3859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nguyen, C L, Georgiou, O & Suppakitpaisarn, V 2019, Improved Localization Accuracy Using Machine Learning : Predicting and Refining RSS Measurements. in 2018 IEEE Globecom Workshops, GC Wkshps 2018 : Proceedings of a meeting held 9-13 December 2018, Abu Dhabi, United Arab Emirates.., 8644270, Institute of Electrical and Electronics Engineers (IEEE), pp. 1506-1512. https://doi.org/10.1109/GLOCOMW.2018.8644270","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:research-information.bris.ac.uk:publications/4cb178e0-b7be-4940-bea0-9afc43fa3859","is_oa":false,"landing_page_url":"https://globecom2018.ieee-globecom.org/workshop/ws-18-mlcomm-machine-learning-communications/program","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:research-information.bris.ac.uk:openaire/4cb178e0-b7be-4940-bea0-9afc43fa3859","is_oa":true,"landing_page_url":"https://hdl.handle.net/1983/4cb178e0-b7be-4940-bea0-9afc43fa3859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nguyen, C L, Georgiou, O & Suppakitpaisarn, V 2019, Improved Localization Accuracy Using Machine Learning : Predicting and Refining RSS Measurements. in 2018 IEEE Globecom Workshops, GC Wkshps 2018 : Proceedings of a meeting held 9-13 December 2018, Abu Dhabi, United Arab Emirates.., 8644270, Institute of Electrical and Electronics Engineers (IEEE), pp. 1506-1512. https://doi.org/10.1109/GLOCOMW.2018.8644270","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1771819215","https://openalex.org/W1983553030","https://openalex.org/W1988543519","https://openalex.org/W2006805041","https://openalex.org/W2049444669","https://openalex.org/W2055836310","https://openalex.org/W2088178750","https://openalex.org/W2099567032","https://openalex.org/W2111619626","https://openalex.org/W2116070975","https://openalex.org/W2437618134","https://openalex.org/W2534482937","https://openalex.org/W2762508189","https://openalex.org/W2768759593","https://openalex.org/W2963211309"],"related_works":["https://openalex.org/W4384112194","https://openalex.org/W2783354812","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W426968574","https://openalex.org/W2365639220","https://openalex.org/W2382520895","https://openalex.org/W2341433667"],"abstract_inverted_index":{"Wireless":[0],"localization":[1,47,106],"methods":[2,53],"are":[3,14,96],"often":[4],"subject":[5],"to":[6,9,16,28,37,54,75,83,104],"errors":[7],"due":[8],"radio":[10],"signal":[11],"fluctuations":[12],"that":[13,42],"used":[15],"estimate":[17],"inter-device":[18],"separation":[19],"distances.":[20],"We":[21],"propose":[22],"a":[23,85],"novel":[24],"method":[25],"called":[26],"MLRefine":[27,49,69],"counter":[29],"these":[30],"effects":[31],"by":[32],"refining":[33],"RSS":[34,67,80,90,94],"measurement":[35,81],"data":[36],"obtain":[38],"more":[39],"accurate":[40,59],"values":[41,60,82,95],"can":[43],"enhance":[44],"ranging":[45],"and":[46,61,101],"accuracies.":[48],"uses":[50],"machine":[51],"learning":[52],"model":[55,74],"the":[56,72],"relationship":[57],"between":[58],"features":[62,76],"extracted":[63,77],"from":[64,78],"in":[65],"silico":[66],"values.":[68,91],"then":[70],"applies":[71],"trained":[73],"real":[79,102],"return":[84],"predicted":[86],"set":[87],"of":[88],"refined":[89,93],"The":[92],"shown":[97],"through":[98],"computer":[99],"simulations":[100],"experiments":[103],"improve":[105],"accuracy.":[107]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
