{"id":"https://openalex.org/W4400072183","doi":"https://doi.org/10.1109/jiot.2024.3420122","title":"C2R: A Novel ANN Architecture for Boosting Indoor Positioning With Scarce Data","display_name":"C2R: A Novel ANN Architecture for Boosting Indoor Positioning With Scarce Data","publication_year":2024,"publication_date":"2024-06-27","ids":{"openalex":"https://openalex.org/W4400072183","doi":"https://doi.org/10.1109/jiot.2024.3420122"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3420122","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3420122","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/jiot.2024.3420122","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074836816","display_name":"Roman Klus","orcid":"https://orcid.org/0000-0002-0641-5931"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Roman Klus","raw_affiliation_strings":["Department of Electrical Engineering, Tampere University, Tampere, Finland"],"raw_orcid":"https://orcid.org/0000-0002-0641-5931","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044609448","display_name":"Jukka Talvitie","orcid":"https://orcid.org/0000-0001-7685-7666"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Jukka Talvitie","raw_affiliation_strings":["Department of Electrical Engineering, Tampere University, Tampere, Finland"],"raw_orcid":"https://orcid.org/0000-0001-7685-7666","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059422222","display_name":"Joaqu\u00edn Torres-Sospedra","orcid":"https://orcid.org/0000-0003-4338-4334"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joaqu\u00edn Torres-Sospedra","raw_affiliation_strings":["Departament d&#x2019;Inform&#x00E0;tica, Universitat de Val&#x00E8;ncia, Burjassot, Spain"],"raw_orcid":"https://orcid.org/0000-0003-4338-4334","affiliations":[{"raw_affiliation_string":"Departament d&#x2019;Inform&#x00E0;tica, Universitat de Val&#x00E8;ncia, Burjassot, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011996470","display_name":"Darwin Quezada-Gaibor","orcid":"https://orcid.org/0000-0002-8064-9955"},"institutions":[{"id":"https://openalex.org/I10902133","display_name":"Universitat Jaume I","ror":"https://ror.org/02ws1xc11","country_code":"ES","type":"education","lineage":["https://openalex.org/I10902133"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Darwin P. Quezada Gaibor","raw_affiliation_strings":["Institute of New Imaging Technologies, Universitat Jaume I, Castell&#x00F3;n de la Plana, Spain"],"raw_orcid":"https://orcid.org/0000-0002-8064-9955","affiliations":[{"raw_affiliation_string":"Institute of New Imaging Technologies, Universitat Jaume I, Castell&#x00F3;n de la Plana, Spain","institution_ids":["https://openalex.org/I10902133"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016376461","display_name":"Sven Casteleyn","orcid":"https://orcid.org/0000-0003-0572-5716"},"institutions":[{"id":"https://openalex.org/I10902133","display_name":"Universitat Jaume I","ror":"https://ror.org/02ws1xc11","country_code":"ES","type":"education","lineage":["https://openalex.org/I10902133"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Sven Casteleyn","raw_affiliation_strings":["Institute of New Imaging Technologies, Universitat Jaume I, Castell&#x00F3;n de la Plana, Spain"],"raw_orcid":"https://orcid.org/0000-0003-0572-5716","affiliations":[{"raw_affiliation_string":"Institute of New Imaging Technologies, Universitat Jaume I, Castell&#x00F3;n de la Plana, Spain","institution_ids":["https://openalex.org/I10902133"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008128583","display_name":"Danijela \u010cabri\u0107","orcid":"https://orcid.org/0000-0002-5967-2683"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danijela Cabric","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5967-2683","affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054197935","display_name":"Mikko Valkama","orcid":"https://orcid.org/0000-0003-0361-0800"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Mikko Valkama","raw_affiliation_strings":["Department of Electrical Engineering, Tampere University, Tampere, Finland"],"raw_orcid":"https://orcid.org/0000-0003-0361-0800","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8654,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72623389,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"11","issue":"20","first_page":"32868","last_page":"32882"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9390000104904175,"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":0.9160000085830688,"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":0.8338487148284912},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.653636634349823},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6527048349380493},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5628453493118286},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5438029170036316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49313849210739136},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.47857025265693665},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44833239912986755},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44433778524398804},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4219474494457245},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09657639265060425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08225899934768677}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8338487148284912},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.653636634349823},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6527048349380493},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5628453493118286},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5438029170036316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49313849210739136},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.47857025265693665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44833239912986755},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44433778524398804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4219474494457245},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09657639265060425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08225899934768677},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3420122","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3420122","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/jiot.2024.3420122","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3420122","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1129888960","display_name":null,"funder_award_id":"338224","funder_id":"https://openalex.org/F4320322887","funder_display_name":"The Research Council"},{"id":"https://openalex.org/G355805632","display_name":null,"funder_award_id":"328214","funder_id":"https://openalex.org/F4320322887","funder_display_name":"The Research Council"},{"id":"https://openalex.org/G3921998432","display_name":null,"funder_award_id":"CIDEXG/2023/17","funder_id":"https://openalex.org/F4320321864","funder_display_name":"Generalitat Valenciana"},{"id":"https://openalex.org/G3992224070","display_name":null,"funder_award_id":"357730","funder_id":"https://openalex.org/F4320322887","funder_display_name":"The Research Council"},{"id":"https://openalex.org/G4129424702","display_name":null,"funder_award_id":"20220411","funder_id":"https://openalex.org/F4320322426","funder_display_name":"Nokia Foundation"},{"id":"https://openalex.org/G5152493591","display_name":null,"funder_award_id":"323244","funder_id":"https://openalex.org/F4320322887","funder_display_name":"The Research Council"},{"id":"https://openalex.org/G6151652769","display_name":null,"funder_award_id":"319994","funder_id":"https://openalex.org/F4320322887","funder_display_name":"The Research Council"}],"funders":[{"id":"https://openalex.org/F4320321864","display_name":"Generalitat Valenciana","ror":"https://ror.org/0097mvx21"},{"id":"https://openalex.org/F4320322426","display_name":"Nokia Foundation","ror":"https://ror.org/0401nzk46"},{"id":"https://openalex.org/F4320322887","display_name":"The Research Council","ror":"https://ror.org/03tcppy59"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1630879738","https://openalex.org/W1825073837","https://openalex.org/W1970826986","https://openalex.org/W2089947415","https://openalex.org/W2095045496","https://openalex.org/W2163314541","https://openalex.org/W2170102584","https://openalex.org/W2240192984","https://openalex.org/W2309512289","https://openalex.org/W2345276999","https://openalex.org/W2511670370","https://openalex.org/W2515976631","https://openalex.org/W2591492581","https://openalex.org/W2599936006","https://openalex.org/W2606436201","https://openalex.org/W2753866421","https://openalex.org/W2762342550","https://openalex.org/W2769849104","https://openalex.org/W2787516708","https://openalex.org/W2792367815","https://openalex.org/W2810515659","https://openalex.org/W2886915282","https://openalex.org/W2891206451","https://openalex.org/W2899663614","https://openalex.org/W2901531104","https://openalex.org/W2906432502","https://openalex.org/W2911091386","https://openalex.org/W2921113176","https://openalex.org/W2946902499","https://openalex.org/W2967881340","https://openalex.org/W2981857663","https://openalex.org/W2998138386","https://openalex.org/W3009434745","https://openalex.org/W3010719864","https://openalex.org/W3039795369","https://openalex.org/W3045554119","https://openalex.org/W3067373627","https://openalex.org/W3088141759","https://openalex.org/W3090615956","https://openalex.org/W3093709762","https://openalex.org/W3109816597","https://openalex.org/W3126796721","https://openalex.org/W3131351338","https://openalex.org/W3134406316","https://openalex.org/W3149937136","https://openalex.org/W3172469842","https://openalex.org/W3183881215","https://openalex.org/W3196647917","https://openalex.org/W3199469864","https://openalex.org/W3208896133","https://openalex.org/W4206418347","https://openalex.org/W4283023197","https://openalex.org/W4283208913","https://openalex.org/W4283259886","https://openalex.org/W4312687417","https://openalex.org/W4312844021","https://openalex.org/W4313639441","https://openalex.org/W4365504037","https://openalex.org/W4376481186","https://openalex.org/W4377085189","https://openalex.org/W4388579631","https://openalex.org/W4393416021","https://openalex.org/W4393894610","https://openalex.org/W4395028056","https://openalex.org/W6631190155","https://openalex.org/W6755977528"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W2378211422","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W4239293476"],"abstract_inverted_index":{"Improving":[0],"the":[1,26,33,50,59,68,84,91,113,136,149,152,156,192,196,206,217,225],"performance":[2],"of":[3,86,142],"Artificial":[4],"Neural":[5],"Network":[6],"(ANN)":[7],"regression":[8,34,52],"models":[9],"on":[10],"small":[11],"or":[12],"scarce":[13],"datasets,":[14,151],"such":[15,29],"as":[16,36,209],"wireless":[17],"network":[18,182],"positioning":[19,115,123,143,158],"data,":[20],"can":[21,82],"be":[22],"realized":[23],"by":[24,40,94,160,175],"simplifying":[25],"task.":[27],"One":[28],"approach":[30],"includes":[31],"implementing":[32,95],"model":[35,70,128],"a":[37,41,62,72,96,179,201],"classifier,":[38],"followed":[39],"probabilistic":[42,92],"mapping":[43,93],"algorithm":[44],"that":[45,66],"transforms":[46,67],"class":[47],"probabilities":[48],"into":[49,71],"multi-dimensional":[51],"output.":[53],"In":[54],"this":[55],"work,":[56],"we":[57],"propose":[58],"so-called":[60],"c2r,":[61],"novel":[63],"ANN-based":[64],"architecture":[65],"classification":[69],"robust":[73],"regressor,":[74],"while":[75,190],"enabling":[76],"end-to-end":[77],"training.":[78],"The":[79,105,125],"proposed":[80,106,126,153,197],"solution":[81,107],"remove":[83],"impact":[85],"less":[87],"likely":[88],"classes":[89],"from":[90,168,184],"novel,":[97],"trainable":[98],"differential":[99],"thresholded":[100],"Rectified":[101],"Linear":[102],"Unit":[103],"layer.":[104],"is":[108,129,220],"introduced":[109],"and":[110,174,213],"evaluated":[111],"in":[112,140,216,224],"indoor":[114],"application":[116],"domain,":[117],"using":[118],"23":[119,150],"real-world,":[120],"openly":[121],"available":[122],"datasets.":[124],"C2R":[127],"shown":[130,210],"to":[131,163,171,178,187,205],"achieve":[132],"significant":[133,222],"improvements":[134],"over":[135],"numerous":[137],"benchmark":[138],"methods":[139],"terms":[141],"accuracy.":[144],"Specifically,":[145],"when":[146],"averaged":[147],"across":[148],"c2r":[154],"improves":[155],"mean":[157],"error":[159],"7.9%":[161],"compared":[162,177],"weighted":[164],"knn":[165],"with":[166],"k=3,":[167],"5.43":[169],"m":[170,186],"5.00":[172,188],"m,":[173,189],"15.4%":[176],"dense":[180],"neural":[181],"(DNN),":[183],"5.91":[185],"adapting":[191],"learned":[193],"threshold.":[194],"Finally,":[195],"method":[198],"adds":[199],"only":[200],"single":[202],"training":[203],"parameter":[204],"ann,":[207],"thus":[208],"through":[211],"analytical":[212],"empirical":[214],"means":[215],"article,":[218],"there":[219],"no":[221],"increase":[223],"computational":[226],"complexity.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2024-06-28T00:00:00"}
