{"id":"https://openalex.org/W7117653315","doi":"https://doi.org/10.1109/tmc.2025.3649637","title":"Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints","display_name":"Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints","publication_year":2025,"publication_date":"2025-12-31","ids":{"openalex":"https://openalex.org/W7117653315","doi":"https://doi.org/10.1109/tmc.2025.3649637"},"language":null,"primary_location":{"id":"doi:10.1109/tmc.2025.3649637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3649637","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-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":"Wenzhong Yan","orcid":"https://orcid.org/0009-0009-7768-4950"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhong Yan","raw_affiliation_strings":["School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-7768-4950","affiliations":[{"raw_affiliation_string":"School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102318704","display_name":"Feng Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Yin","raw_affiliation_strings":["School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5754-9246","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121609384","display_name":"Jun Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Gao","raw_affiliation_strings":["School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0005-9256-4747","affiliations":[{"raw_affiliation_string":"School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121630000","display_name":"Ao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ao Wang","raw_affiliation_strings":["School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102957353","display_name":"Yang Tian","orcid":"https://orcid.org/0000-0002-2492-6719"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Tian","raw_affiliation_strings":["Huawei Technologies Company, Ltd., Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Technologies Company, Ltd., Shanghai, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101602321","display_name":"Ruizhi Chen","orcid":"https://orcid.org/0000-0001-7219-4658"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruizhi Chen","raw_affiliation_strings":["School of Data Science, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9253,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79194381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"25","issue":"6","first_page":"8718","last_page":"8734"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9253000020980835,"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":0.9253000020980835,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006000000052154064,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.005799999926239252,"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/fingerprint","display_name":"Fingerprint (computing)","score":0.6421999931335449},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.6323999762535095},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6208999752998352},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5570999979972839},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5547999739646912},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5121999979019165},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.47450000047683716},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43479999899864197},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.4205999970436096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8579000234603882},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.6421999931335449},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.6323999762535095},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6208999752998352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029000282287598},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5570999979972839},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5547999739646912},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5121999979019165},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.47450000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46650001406669617},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.4205999970436096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41929998993873596},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C13251829","wikidata":"https://www.wikidata.org/wiki/Q3085841","display_name":"Dense graph","level":5,"score":0.32910001277923584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.31029999256134033},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.27140000462532043}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2025.3649637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3649637","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1630879738","https://openalex.org/W1893431417","https://openalex.org/W2051376734","https://openalex.org/W2052850027","https://openalex.org/W2094655360","https://openalex.org/W2095705027","https://openalex.org/W2143228105","https://openalex.org/W2152079373","https://openalex.org/W2170102584","https://openalex.org/W2240192984","https://openalex.org/W2571881496","https://openalex.org/W2732107624","https://openalex.org/W2753866421","https://openalex.org/W2922174720","https://openalex.org/W2944172587","https://openalex.org/W2972434579","https://openalex.org/W2980021985","https://openalex.org/W3084212472","https://openalex.org/W3113374561","https://openalex.org/W3126469890","https://openalex.org/W3162881616","https://openalex.org/W3191587252","https://openalex.org/W4226026671","https://openalex.org/W4226208255","https://openalex.org/W4290996518","https://openalex.org/W4312989694","https://openalex.org/W4385245566","https://openalex.org/W4387252280","https://openalex.org/W4387449321","https://openalex.org/W4392903512","https://openalex.org/W4412871812"],"related_works":[],"abstract_inverted_index":{"Fingerprint-based":[0],"indoor":[1,225],"localization":[2,22,226,242],"is":[3],"often":[4],"labor-intensive":[5],"due":[6],"to":[7,88,120,185],"the":[8,38,94,116,138,149,165,175,178,182,187,239],"need":[9],"for":[10,97,222],"dense":[11],"grids":[12],"and":[13,18,45,71,77,126,154,181,207,234],"repeated":[14],"measurements":[15],"across":[16,210,252],"time":[17],"space.":[19],"Maintaining":[20],"high":[21],"accuracy":[23],"with":[24,84],"extremely":[25,191],"sparse":[26,192,250],"fingerprints":[27,156,251],"remains":[28],"a":[29,54,78,142,219],"persistent":[30],"challenge.":[31],"Existing":[32],"benchmark":[33],"methods":[34,248],"primarily":[35],"rely":[36],"on":[37,231],"measured":[39],"fingerprints,":[40,76,125],"while":[41],"neglecting":[42],"valuable":[43,220],"spatial":[44,68],"environmental":[46,86,133],"characteristics.":[47],"To":[48,92,194],"address":[49,186],"this":[50],"issue,":[51],"we":[52,100,163,198],"propose":[53,164],"systematic":[55],"integration":[56],"of":[57,66,177],"an":[58],"Attentional":[59,166],"Graph":[60,167],"Neural":[61],"Network":[62],"(AGNN)":[63],"model,":[64],"capable":[65],"learning":[67],"adjacency":[69],"relationships":[70],"aggregating":[72],"information":[73,122],"from":[74,123,202],"neighboring":[75],"meta-learning":[79,139,183],"framework":[80,140,184],"that":[81,238],"utilizes":[82],"datasets":[83,201,214,236],"similar":[85],"characteristics":[87],"enhance":[89],"model":[90,119,173,180],"training.":[91],"minimize":[93,148],"labor":[95],"required":[96],"fingerprint":[98,109,130],"collection,":[99],"introduce":[101],"two":[102],"novel":[103,161,172],"data":[104],"augmentation":[105,110,131],"strategies:":[106],"1)":[107],"unlabeled":[108,124],"using":[111,249],"moving":[112],"platforms,":[113],"which":[114,136,146],"enables":[115],"semi-supervised":[117],"AGNN":[118,179],"incorporate":[121],"2)":[127],"synthetic":[128,153,233],"labeled":[129],"through":[132,141],"digital":[134],"twins,":[135],"enhances":[137],"practical":[143],"distribution":[144],"alignment,":[145],"can":[147,215],"feature":[150],"discrepancy":[151],"between":[152],"real-world":[155,235],"effectively.":[157],"By":[158],"integrating":[159],"these":[160],"modules,":[162],"Meta-Learning":[168],"(AGML)":[169],"model.":[170],"This":[171],"combines":[174],"strengths":[176],"challenges":[188],"posed":[189],"by":[190],"fingerprints.":[193],"validate":[195],"our":[196],"approach,":[197],"collected":[199],"multiple":[200],"both":[203,232],"consumer-grade":[204],"WiFi":[205],"devices":[206],"professional":[208],"equipment":[209],"diverse":[211],"environments.":[212],"These":[213],"also":[216],"serve":[217],"as":[218],"resource":[221],"benchmarking":[223],"fingerprint-based":[224],"methods.":[227],"Extensive":[228],"experiments":[229],"conducted":[230],"demonstrate":[237],"AGML":[240],"model-based":[241],"method":[243],"consistently":[244],"outperforms":[245],"all":[246,253],"baseline":[247],"evaluated":[254],"metrics.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-09T06:09:20.037420","created_date":"2025-12-31T00:00:00"}