{"id":"https://openalex.org/W4362653662","doi":"https://doi.org/10.1109/tii.2023.3254668","title":"TinyAD: Memory-Efficient Anomaly Detection for Time-Series Data in Industrial IoT","display_name":"TinyAD: Memory-Efficient Anomaly Detection for Time-Series Data in Industrial IoT","publication_year":2023,"publication_date":"2023-04-05","ids":{"openalex":"https://openalex.org/W4362653662","doi":"https://doi.org/10.1109/tii.2023.3254668"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2023.3254668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3254668","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","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":"https://openalex.org/A5101571850","display_name":"Yuting Sun","orcid":"https://orcid.org/0000-0002-2592-1290"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuting Sun","raw_affiliation_strings":["School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461265","display_name":"Tong Chen","orcid":"https://orcid.org/0000-0001-7269-146X"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tong Chen","raw_affiliation_strings":["School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7269-146X","affiliations":[{"raw_affiliation_string":"School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051219382","display_name":"Quoc Viet Hung Nguyen","orcid":"https://orcid.org/0000-0002-9687-1315"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Quoc Viet Hung Nguyen","raw_affiliation_strings":["Institute for Integrated and Intelligent Systems, Griffith University, Southport, Australia"],"raw_orcid":"https://orcid.org/0000-0002-9687-1315","affiliations":[{"raw_affiliation_string":"Institute for Integrated and Intelligent Systems, Griffith University, Southport, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088492734","display_name":"Hongzhi Yin","orcid":"https://orcid.org/0000-0003-1395-261X"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hongzhi Yin","raw_affiliation_strings":["School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia"],"raw_orcid":"https://orcid.org/0000-0003-1395-261X","affiliations":[{"raw_affiliation_string":"School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia","institution_ids":["https://openalex.org/I165143802"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.8692,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.96765019,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"20","issue":"1","first_page":"824","last_page":"834"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.7965670824050903},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7910846471786499},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5165264010429382},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4757140278816223},{"id":"https://openalex.org/keywords/memory-management","display_name":"Memory management","score":0.45686787366867065},{"id":"https://openalex.org/keywords/virtual-memory","display_name":"Virtual memory","score":0.4532450735569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4451514482498169},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.41314947605133057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3487861454486847},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30201542377471924},{"id":"https://openalex.org/keywords/semiconductor-memory","display_name":"Semiconductor memory","score":0.2435811161994934},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.17352688312530518}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7965670824050903},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7910846471786499},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5165264010429382},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4757140278816223},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.45686787366867065},{"id":"https://openalex.org/C76399640","wikidata":"https://www.wikidata.org/wiki/Q189401","display_name":"Virtual memory","level":4,"score":0.4532450735569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4451514482498169},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.41314947605133057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3487861454486847},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30201542377471924},{"id":"https://openalex.org/C98986596","wikidata":"https://www.wikidata.org/wiki/Q1143031","display_name":"Semiconductor memory","level":2,"score":0.2435811161994934},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.17352688312530518}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tii.2023.3254668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3254668","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/424370","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/424370","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G1752580696","display_name":"Decentralised Collaborative Predictive Analytics on Personal Smart Devices","funder_award_id":"FT210100624","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W752950670","https://openalex.org/W2194775991","https://openalex.org/W2740570963","https://openalex.org/W2768947629","https://openalex.org/W2776990447","https://openalex.org/W2800569739","https://openalex.org/W2906498146","https://openalex.org/W2962856739","https://openalex.org/W2965762068","https://openalex.org/W2982479999","https://openalex.org/W2984618279","https://openalex.org/W3004268830","https://openalex.org/W3004999940","https://openalex.org/W3034222740","https://openalex.org/W3109600188","https://openalex.org/W3155567600","https://openalex.org/W3177367985","https://openalex.org/W3206585998","https://openalex.org/W4200152289","https://openalex.org/W4205355262","https://openalex.org/W4226066991","https://openalex.org/W4226158669","https://openalex.org/W4283318673","https://openalex.org/W4288057688","https://openalex.org/W6622186613","https://openalex.org/W6753618346","https://openalex.org/W6756753118","https://openalex.org/W6780827055","https://openalex.org/W6803223524","https://openalex.org/W6838539104"],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W3019910406","https://openalex.org/W1976766385","https://openalex.org/W2078640694","https://openalex.org/W261562921","https://openalex.org/W1421493983","https://openalex.org/W4243333834","https://openalex.org/W1698699620","https://openalex.org/W71081774","https://openalex.org/W2055367414"],"abstract_inverted_index":{"Monitoring":[0],"and":[1,40,121,127,163,168,217,237],"detecting":[2],"abnormal":[3],"events":[4],"in":[5,219],"cyber-physical":[6],"systems":[7],"is":[8,30,42,59],"crucial":[9],"to":[10,32,47,62,100,148,191,232],"industrial":[11,19,246],"production.":[12],"With":[13],"the":[14,18,44,50,54,85,130,137,144,150,179,183,193,197,202,210,224],"prevalent":[15],"deployment":[16],"of":[17,21,27,43,88,105,117,154,182,205,226],"Internet":[20],"Things":[22],"(IIoTs),":[23],"an":[24],"enormous":[25],"amount":[26],"time-series":[28],"data":[29,212],"collected":[31],"facilitate":[33,102],"machine":[34],"learning":[35,66],"models":[36,52,67],"for":[37,107,124],"anomaly":[38,109,125],"detection,":[39],"it":[41,58],"utmost":[45],"importance":[46],"directly":[48],"deploy":[49,63],"trained":[51],"on":[53,74,244],"IIoT":[55,77],"devices.":[56],"However,":[57],"most":[60],"challenging":[61],"complex":[64],"deep":[65],"such":[68],"as":[69],"convolutional":[70],"neural":[71],"networks":[72],"(CNNs)":[73],"these":[75,229],"memory-constrained":[76],"devices":[78],"embedded":[79],"with":[80,143,259],"microcontrollers":[81],"(MCUs).":[82],"To":[83],"alleviate":[84],"memory":[86,152,166,181,204,255],"constraints":[87],"MCUs,":[89],"we":[90,112,156],"propose":[91],"a":[92,114,172,188],"novel":[93],"framework":[94,251],"named":[95],"Tiny":[96],"Anomaly":[97],"Detection":[98],"(TinyAD)":[99],"efficiently":[101],"onboard":[103],"inference":[104],"CNNs":[106,120,123],"real-time":[108],"detection.":[110],"First,":[111],"conduct":[113],"comprehensive":[115],"analysis":[116],"depthwise":[118,131,184],"separable":[119,132],"regular":[122],"detection":[126],"find":[128],"that":[129,249],"convolution":[133,185,227],"operation":[134],"can":[135,252],"reduce":[136,149,253],"model":[138],"size":[139],"by":[140,186,208,222,257],"50%\u201390%":[141],"compared":[142],"traditional":[145],"CNNs.":[146],"Then,":[147],"peak":[151,180,203,254],"consumption":[153,256],"CNNs,":[155],"explore":[157],"two":[158],"complementary":[159],"strategies,":[160],"1)":[161],"in-place;":[162],"2)":[164],"patch-by-patch":[165,198],"rescheduling,":[167],"integrate":[169],"them":[170],"into":[171,213],"unified":[173],"framework.":[174],"The":[175],"in-place":[176],"method":[177,199],"decreases":[178],"sparing":[187],"temporary":[189],"buffer":[190],"transfer":[192],"activation":[194],"results,":[195],"while":[196],"further":[200],"reduces":[201],"layer-wise":[206],"execution":[207],"slicing":[209],"input":[211],"corresponding":[214],"receptive":[215],"fields":[216],"executing":[218],"order.":[220],"Furthermore,":[221],"adjusting":[223],"dimension":[225],"filters,":[228],"strategies":[230],"apply":[231],"both":[233],"univariate":[234],"time":[235,239],"series":[236,240],"multidomain":[238],"features.":[241],"Extensive":[242],"experiments":[243],"real-world":[245],"datasets":[247],"show":[248],"our":[250],"2\u20135\u00d7":[258],"negligible":[260],"computation":[261],"overhead.":[262]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":10}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
