{"id":"https://openalex.org/W2615911067","doi":"https://doi.org/10.1109/wocc.2017.7928970","title":"Indoor localization framework with WiFi fingerprinting","display_name":"Indoor localization framework with WiFi fingerprinting","publication_year":2017,"publication_date":"2017-04-01","ids":{"openalex":"https://openalex.org/W2615911067","doi":"https://doi.org/10.1109/wocc.2017.7928970","mag":"2615911067"},"language":"en","primary_location":{"id":"doi:10.1109/wocc.2017.7928970","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocc.2017.7928970","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 26th Wireless and Optical Communication Conference (WOCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5051030661","display_name":"Rajan Khullar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104314","display_name":"New York Institute of Technology","ror":"https://ror.org/01bghzb51","country_code":"US","type":"education","lineage":["https://openalex.org/I4210104314"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajan Khullar","raw_affiliation_strings":["School of Engineering and Computing Sciences, New York Institute of Technology, New York, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Computing Sciences, New York Institute of Technology, New York, NY","institution_ids":["https://openalex.org/I4210104314"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079318596","display_name":"Ziqian Dong","orcid":"https://orcid.org/0000-0003-3937-1311"},"institutions":[{"id":"https://openalex.org/I4210104314","display_name":"New York Institute of Technology","ror":"https://ror.org/01bghzb51","country_code":"US","type":"education","lineage":["https://openalex.org/I4210104314"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziqian Dong","raw_affiliation_strings":["School of Engineering and Computing Sciences, New York Institute of Technology, New York, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Computing Sciences, New York Institute of Technology, New York, NY","institution_ids":["https://openalex.org/I4210104314"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210104314"],"apc_list":null,"apc_paid":null,"fwci":3.4128,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.93272127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"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":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/T10860","display_name":"Speech and Audio Processing","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7751868963241577},{"id":"https://openalex.org/keywords/signal-strength","display_name":"Signal strength","score":0.7577207088470459},{"id":"https://openalex.org/keywords/android","display_name":"Android (operating system)","score":0.5562481880187988},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5326060056686401},{"id":"https://openalex.org/keywords/location-data","display_name":"Location data","score":0.5225072503089905},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4740053117275238},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4350036084651947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39082634449005127},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3809669613838196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36051565408706665},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.176573246717453},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.14088571071624756}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7751868963241577},{"id":"https://openalex.org/C176808163","wikidata":"https://www.wikidata.org/wiki/Q17105794","display_name":"Signal strength","level":3,"score":0.7577207088470459},{"id":"https://openalex.org/C557433098","wikidata":"https://www.wikidata.org/wiki/Q94","display_name":"Android (operating system)","level":2,"score":0.5562481880187988},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5326060056686401},{"id":"https://openalex.org/C2988186277","wikidata":"https://www.wikidata.org/wiki/Q5915793","display_name":"Location data","level":2,"score":0.5225072503089905},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4740053117275238},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4350036084651947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39082634449005127},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3809669613838196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36051565408706665},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.176573246717453},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.14088571071624756},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wocc.2017.7928970","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocc.2017.7928970","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 26th Wireless and Optical Communication Conference (WOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5400000214576721,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320318285","display_name":"New York Institute of Technology","ror":"https://ror.org/01bghzb51"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1520408747","https://openalex.org/W2018235121","https://openalex.org/W2033364470","https://openalex.org/W2293823404"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W1991580985","https://openalex.org/W1980100242","https://openalex.org/W2530420969","https://openalex.org/W2615911067"],"abstract_inverted_index":{"Indoor":[0],"localization":[1,21],"through":[2,39,72],"WiFi":[3,26,40],"fingerprinting":[4],"requires":[5],"a":[6,16,32,46,96],"large":[7],"number":[8],"of":[9,45,99],"fine-grained":[10],"data":[11,17,30,58],"samples.":[12],"This":[13],"study":[14],"presents":[15],"acquisition":[18],"and":[19,36,48,52,70],"indoor":[20],"framework":[22,43],"that":[23,95],"collects":[24],"crowd-sourced":[25],"received":[27],"signal":[28],"strength":[29],"in":[31,63],"metropolitan":[33],"high-rise":[34],"building":[35],"predicts":[37],"location":[38,79,90,105,110],"fingerprinting.":[41],"The":[42,66,108],"consists":[44],"server":[47],"an":[49],"Android":[50],"application":[51],"was":[53,68],"tested":[54],"at":[55],"NYIT":[56],"for":[57,60,88],"collection":[59],"two":[61],"weeks":[62],"December":[64],"2016.":[65],"dataset":[67],"preprocessed":[69],"analyzed":[71],"linear":[73],"support":[74],"vector":[75],"machine":[76],"to":[77,102,116,124],"test":[78],"prediction":[80,91,106,111],"accuracy.":[81,92,107],"Various":[82],"feature":[83],"selection":[84],"schemes":[85],"were":[86],"compared":[87],"their":[89],"We":[93],"show":[94],"small":[97],"subset":[98],"features":[100,120],"suffices":[101],"provide":[103],"high":[104],"average":[109],"accuracy":[112],"increases":[113],"from":[114],"83%":[115],"100%":[117],"when":[118],"time":[119],"are":[121],"considered":[122],"comparing":[123],"using":[125],"only":[126],"spatial":[127],"features.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
