{"id":"https://openalex.org/W4402628004","doi":"https://doi.org/10.1109/apwcs61586.2024.10679309","title":"Utilizing Machine Learning for Indoor Localization with Multiple Wi-Fi Assistance","display_name":"Utilizing Machine Learning for Indoor Localization with Multiple Wi-Fi Assistance","publication_year":2024,"publication_date":"2024-08-21","ids":{"openalex":"https://openalex.org/W4402628004","doi":"https://doi.org/10.1109/apwcs61586.2024.10679309"},"language":"en","primary_location":{"id":"doi:10.1109/apwcs61586.2024.10679309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apwcs61586.2024.10679309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chung-Ruei Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4880106","display_name":"Feng Chia University","ror":"https://ror.org/05vhczg54","country_code":"TW","type":"education","lineage":["https://openalex.org/I4880106"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chung-Ruei Huang","raw_affiliation_strings":["Feng Chia University,Department of Communications Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feng Chia University,Department of Communications Engineering,Taiwan","institution_ids":["https://openalex.org/I4880106"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077684464","display_name":"Ang-Hsun Tsai","orcid":"https://orcid.org/0000-0003-2445-3939"},"institutions":[{"id":"https://openalex.org/I4880106","display_name":"Feng Chia University","ror":"https://ror.org/05vhczg54","country_code":"TW","type":"education","lineage":["https://openalex.org/I4880106"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ang-Hsun Tsai","raw_affiliation_strings":["Feng Chia University,Department of Communications Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feng Chia University,Department of Communications Engineering,Taiwan","institution_ids":["https://openalex.org/I4880106"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087225173","display_name":"Chao-Yang Lee","orcid":"https://orcid.org/0000-0003-3898-3551"},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chao-Yang Lee","raw_affiliation_strings":["National Yunlin University of Science and Technology,Department of Computer Science and Information Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Yunlin University of Science and Technology,Department of Computer Science and Information Engineering,Taiwan","institution_ids":["https://openalex.org/I75357094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1856,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50157068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9976999759674072,"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":0.9976999759674072,"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/T13121","display_name":"Radio Wave Propagation Studies","score":0.9330999851226807,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7130204439163208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38072502613067627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7130204439163208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38072502613067627}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apwcs61586.2024.10679309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apwcs61586.2024.10679309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2550697389","https://openalex.org/W2551883057","https://openalex.org/W2768305371","https://openalex.org/W2905291758","https://openalex.org/W2906809485","https://openalex.org/W3134837419"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"This":[0],"paper":[1],"evaluates":[2],"the":[3,11,15,24,78,87,106,134,147],"efficacy":[4],"of":[5,14,34,56,70,80,100,122,141,149],"diverse":[6],"indoor":[7,167],"positioning":[8,35,154,168],"methods,":[9],"highlighting":[10],"superior":[12],"performance":[13,110],"Backpropagation":[16],"(BP)":[17],"algorithm.":[18,108],"Through":[19],"extensive":[20],"experimentation,":[21],"we":[22,76],"compared":[23],"Mean":[25],"Square":[26],"Error":[27],"(MSE)":[28],"and":[29,42,51,65,72,97,103,125,130,156],"Cumulative":[30],"Distribution":[31],"Function":[32],"(CDF)":[33],"errors":[36],"across":[37],"different":[38,98],"algorithms,":[39],"including":[40],"Fingerprinting":[41,64,88],"Triangulation.":[43],"The":[44],"BP":[45,107,135],"algorithm":[46],"exhibited":[47],"significantly":[48],"higher":[49],"accuracy":[50,155],"stability,":[52],"with":[53,113,126],"a":[54,60],"CDF":[55],"approximately":[57],"0.92":[58],"at":[59,164],"1-meter":[61],"MSE,":[62],"surpassing":[63],"Triangulation,":[66],"which":[67],"achieved":[68],"CDFs":[69],"0.77":[71],"0.06,":[73],"respectively.":[74],"Moreover,":[75],"examined":[77],"impact":[79],"varying":[81],"<tex":[82,92,114],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[83,93,115],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$K$</tex>":[84],"values":[85],"in":[86,105,138],"method":[89],"based":[90],"on":[91],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$K$</tex>-Nearest":[94],"Neighbors":[95],"(KNN)":[96],"configurations":[99],"hidden":[101,128],"layers":[102,129],"neurons":[104,132],"Optimal":[109],"was":[111],"observed":[112],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$K=2$</tex>":[116],"for":[117,133,152,160],"Fingerprinting,":[118],"yielding":[119],"an":[120,139],"MSE":[121,140],"1.5":[123],"meters,":[124],"22":[127],"47":[131],"algorithm,":[136],"resulting":[137],"0.84":[142],"meters.":[143],"These":[144],"findings":[145],"underscore":[146],"significance":[148],"parameter":[150],"optimization":[151],"enhancing":[153,166],"suggest":[157],"potential":[158],"avenues":[159],"future":[161],"research":[162],"aimed":[163],"further":[165],"systems.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
