{"id":"https://openalex.org/W4416591452","doi":"https://doi.org/10.1016/j.comnet.2026.112309","title":"SMoRFFI: A large-scale same-model 2.4 GHz Wi-Fi dataset and reproducible framework for RF fingerprinting","display_name":"SMoRFFI: A large-scale same-model 2.4 GHz Wi-Fi dataset and reproducible framework for RF fingerprinting","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W4416591452","doi":"https://doi.org/10.1016/j.comnet.2026.112309"},"language":"en","primary_location":{"id":"doi:10.1016/j.comnet.2026.112309","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.comnet.2026.112309","pdf_url":null,"source":{"id":"https://openalex.org/S63392143","display_name":"Computer Networks","issn_l":"1389-1286","issn":["1389-1286","1872-7069"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Networks","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.comnet.2026.112309","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zewei Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I84400586","display_name":"Future University Hakodate","ror":"https://ror.org/05szw2z23","country_code":"JP","type":"education","lineage":["https://openalex.org/I84400586"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zewei Guo","raw_affiliation_strings":["Future University Hakodate, Hakodate, 041-8655, Hokkaido, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Future University Hakodate, Hakodate, 041-8655, Hokkaido, Japan","institution_ids":["https://openalex.org/I84400586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100526280","display_name":"Zhen Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141715","display_name":"Keio University Shonan Fujisawa","ror":"https://ror.org/047khpn70","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210141715"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhen Jia","raw_affiliation_strings":["Keio University, Fujisawa, 108-8345, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University, Fujisawa, 108-8345, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210141715"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinxiao Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I165522056","display_name":"Tokyo Denki University","ror":"https://ror.org/01pa62v70","country_code":"JP","type":"education","lineage":["https://openalex.org/I165522056"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinxiao Zhu","raw_affiliation_strings":["Tokyo Denki University, Tokyo, 120-8551, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Denki University, Tokyo, 120-8551, Tokyo, Japan","institution_ids":["https://openalex.org/I165522056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083410012","display_name":"Wenhao Huang","orcid":"https://orcid.org/0000-0002-0036-6278"},"institutions":[{"id":"https://openalex.org/I4210141715","display_name":"Keio University Shonan Fujisawa","ror":"https://ror.org/047khpn70","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210141715"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wenhao Huang","raw_affiliation_strings":["Keio University, Fujisawa, 108-8345, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University, Fujisawa, 108-8345, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210141715"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104109242","display_name":"Yin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I193758575","display_name":"Reitaku University","ror":"https://ror.org/02rwyg032","country_code":"JP","type":"education","lineage":["https://openalex.org/I193758575"]},{"id":"https://openalex.org/I4210141715","display_name":"Keio University Shonan Fujisawa","ror":"https://ror.org/047khpn70","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210141715"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yin Chen","raw_affiliation_strings":["Keio University, Fujisawa, 108-8345, Kanagawa, Japan","Reitaku University, Kashiwa, 277-8686, Chiba, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2652-8941","affiliations":[{"raw_affiliation_string":"Keio University, Fujisawa, 108-8345, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210141715"]},{"raw_affiliation_string":"Reitaku University, Kashiwa, 277-8686, Chiba, Japan","institution_ids":["https://openalex.org/I193758575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5104109242"],"corresponding_institution_ids":["https://openalex.org/I193758575","https://openalex.org/I4210141715"],"apc_list":{"value":2460,"currency":"USD","value_usd":2460},"apc_paid":{"value":2460,"currency":"USD","value_usd":2460},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01785017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"282","issue":null,"first_page":"112309","last_page":"112309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9664000272750854,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9664000272750854,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.008100000210106373,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.0017999999690800905,"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/pipeline","display_name":"Pipeline (software)","score":0.6575000286102295},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5821999907493591},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5681999921798706},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4546000063419342},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.4413999915122986},{"id":"https://openalex.org/keywords/radio-frequency-identification","display_name":"Radio-frequency identification","score":0.3804999887943268},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3564000129699707}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6721000075340271},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6575000286102295},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5821999907493591},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5681999921798706},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4237000048160553},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39419999718666077},{"id":"https://openalex.org/C204222849","wikidata":"https://www.wikidata.org/wiki/Q104954","display_name":"Radio-frequency identification","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.28870001435279846},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1016/j.comnet.2026.112309","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.comnet.2026.112309","pdf_url":null,"source":{"id":"https://openalex.org/S63392143","display_name":"Computer Networks","issn_l":"1389-1286","issn":["1389-1286","1872-7069"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Networks","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2511.07770","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07770","pdf_url":"https://arxiv.org/pdf/2511.07770","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.07770","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.07770","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1016/j.comnet.2026.112309","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.comnet.2026.112309","pdf_url":null,"source":{"id":"https://openalex.org/S63392143","display_name":"Computer Networks","issn_l":"1389-1286","issn":["1389-1286","1872-7069"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Networks","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5081563838","display_name":"Physical layer authentication of IoT devices in the 6G era","funder_award_id":"24K07482","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5865746565","display_name":"\u30a2\u30d0\u30bf\u30fc\u3092\u5b89\u5168\u304b\u3064\u4fe1\u983c\u3057\u3066\u5229\u7528\u3067\u304d\u308b\u793e\u4f1a\u306e\u5b9f\u73fe","funder_award_id":"JPMJMS2215","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"},{"id":"https://openalex.org/F4320338247","display_name":"Moonshot Research and Development Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Radio":[0],"frequency":[1],"(RF)":[2],"fingerprinting":[3,27],"exploits":[4],"hardware":[5,21],"imperfections":[6],"for":[7,25],"device":[8,32],"identification,":[9],"but":[10],"distinguishing":[11],"between":[12],"same-model":[13,58,72],"devices":[14,59],"remains":[15],"challenging":[16],"due":[17],"to":[18,106,121],"their":[19],"minimal":[20],"variations.":[22],"Existing":[23],"datasets":[24],"RF":[26,92],"are":[28],"constrained":[29],"by":[30],"small":[31],"scales":[33],"and":[34,41,88],"heterogeneous":[35],"models,":[36],"which":[37,78],"hinder":[38],"robust":[39],"training":[40],"fair":[42],"evaluation":[43],"of":[44,57],"machine":[45],"learning":[46],"methods.":[47],"To":[48],"address":[49],"this":[50,110,127],"gap,":[51],"we":[52],"introduce":[53],"a":[54,99,112,119],"large-scale":[55],"dataset":[56,67],"along":[60],"with":[61],"an":[62],"open-source":[63],"experimental":[64],"framework.":[65],"The":[66,94],"is":[68,116],"built":[69],"using":[70],"123":[71],"commercial":[73],"IEEE":[74],"802.11":[75],"g":[76],"devices,":[77],"contain":[79],"35.42":[80],"million":[81,91],"raw":[82],"I/Q":[83],"samples":[84],"from":[85,103],"the":[86],"preambles":[87],"corresponding":[89],"1.85":[90],"features.":[93],"accompanying":[95],"framework":[96],"further":[97],"provides":[98],"fully":[100],"reproducible":[101],"pipeline":[102],"data":[104],"collection":[105],"performance":[107],"evaluation.":[108],"Within":[109],"framework,":[111],"Random":[113],"Forest\u2013based":[114],"algorithm":[115],"implemented":[117],"as":[118],"baseline":[120],"achieve":[122],"88.6%":[123],"identification":[124],"accuracy":[125],"on":[126],"dataset.":[128]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-11-13T00:00:00"}
