{"id":"https://openalex.org/W2886522435","doi":"https://doi.org/10.1109/icufn.2018.8436598","title":"Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network","display_name":"Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2886522435","doi":"https://doi.org/10.1109/icufn.2018.8436598","mag":"2886522435"},"language":"en","primary_location":{"id":"doi:10.1109/icufn.2018.8436598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2018.8436598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","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":"https://openalex.org/A5026690758","display_name":"Jinwoo Jang","orcid":"https://orcid.org/0000-0002-3657-698X"},"institutions":[{"id":"https://openalex.org/I57664883","display_name":"Ajou University","ror":"https://ror.org/03tzb2h73","country_code":"KR","type":"education","lineage":["https://openalex.org/I57664883"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin-Woo Jang","raw_affiliation_strings":["Ajou University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ajou University, Suwon, Korea","institution_ids":["https://openalex.org/I57664883"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084652371","display_name":"Song\u2010Nam Hong","orcid":"https://orcid.org/0000-0002-9535-2521"},"institutions":[{"id":"https://openalex.org/I57664883","display_name":"Ajou University","ror":"https://ror.org/03tzb2h73","country_code":"KR","type":"education","lineage":["https://openalex.org/I57664883"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Song-Nam Hong","raw_affiliation_strings":["Ajou University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ajou University, Suwon, Korea","institution_ids":["https://openalex.org/I57664883"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9282,"has_fulltext":false,"cited_by_count":111,"citation_normalized_percentile":{"value":0.9419762,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"753","last_page":"758"},"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":0.9955999851226807,"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":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.8404414057731628},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8385010957717896},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5961119532585144},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5359746813774109},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5155684947967529},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5011913776397705},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.49545133113861084},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4708136022090912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4547341465950012},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39162132143974304},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3598354458808899},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3064388632774353},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.14649397134780884},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07506728172302246},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.06833860278129578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8404414057731628},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8385010957717896},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5961119532585144},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5359746813774109},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5155684947967529},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5011913776397705},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.49545133113861084},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4708136022090912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4547341465950012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39162132143974304},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3598354458808899},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3064388632774353},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.14649397134780884},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07506728172302246},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.06833860278129578},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icufn.2018.8436598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2018.8436598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","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":16,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1630879738","https://openalex.org/W1981831056","https://openalex.org/W2073221271","https://openalex.org/W2100989187","https://openalex.org/W2156163116","https://openalex.org/W2173462126","https://openalex.org/W2183609727","https://openalex.org/W2187263011","https://openalex.org/W2309512289","https://openalex.org/W2460055519","https://openalex.org/W2552911271","https://openalex.org/W2774684174","https://openalex.org/W2964121744","https://openalex.org/W3120740533","https://openalex.org/W6746752842"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W41015297"],"abstract_inverted_index":{"Indoor":[0],"localization":[1,109],"has":[2],"been":[3],"an":[4,54],"active":[5],"research":[6],"field":[7],"for":[8],"decades,":[9],"due":[10],"to":[11,42,64,122],"its":[12],"wide":[13],"range":[14],"of":[15,31,67,83,126,134],"applications.":[16],"WiFi":[17,26,43],"fingerprinting,":[18],"which":[19,89],"estimates":[20],"the":[21,62,65,98,116,123,132,149,155],"user's":[22],"locations":[23],"using":[24,159],"pre-collecting":[25],"signals":[27,69,128],"as":[28,129,138,140],"references,":[29],"is":[30,61],"particular":[32],"interest":[33],"since":[34],"these":[35],"days,":[36],"every":[37],"user":[38],"can":[39,119,153],"easily":[40],"access":[41],"networks.":[44],"Among":[45],"numerous":[46],"methods,":[47],"deep-neural-network":[48],"(DNN)":[49],"based":[50,108],"methods":[51,158],"have":[52],"shown":[53],"attractive":[55],"performance":[56],"but":[57],"their":[58],"major":[59],"drawback":[60],"sensitivity":[63],"fluctuation":[66],"received":[68,127],"(caused":[70],"by":[71,101],"multipaths).":[72],"To":[73],"yield":[74],"a":[75,79,91,103,135],"satisfactory":[76],"performance,":[77],"thus,":[78],"sufficiently":[80],"large":[81],"number":[82],"possible":[84],"cases":[85],"should":[86],"be":[87,120],"trained,":[88],"costs":[90],"lot.":[92],"In":[93],"this":[94],"paper,":[95],"we":[96,146],"address":[97],"above":[99],"problem":[100],"presenting":[102],"convolutional":[104],"neural":[105],"network":[106],"(CNN)":[107],"method.":[110],"As":[111],"success":[112],"in":[113,164],"image":[114],"classifications,":[115],"proposed":[117,150],"method":[118,152],"robust":[121],"small":[124],"changes":[125],"it":[130],"exploits":[131],"topology":[133],"radio":[136],"map":[137],"well":[139],"signal":[141],"strengths.":[142],"Via":[143],"experimental":[144],"results,":[145],"demonstrate":[147],"that":[148],"CNN":[151],"outperform":[154],"other":[156],"DNN-based":[157],"publicly":[160],"available":[161],"datasets":[162],"provided":[163],"IPIN":[165],"2015.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":26},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}