{"id":"https://openalex.org/W3191769047","doi":"https://doi.org/10.1145/3480172","title":"Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach","display_name":"Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach","publication_year":2021,"publication_date":"2021-10-27","ids":{"openalex":"https://openalex.org/W3191769047","doi":"https://doi.org/10.1145/3480172","mag":"3191769047"},"language":"en","primary_location":{"id":"doi:10.1145/3480172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480172","pdf_url":null,"source":{"id":"https://openalex.org/S4210175912","display_name":"ACM Transactions on Internet of Things","issn_l":"2577-6207","issn":["2577-6207","2691-1914"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Internet of Things","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2108.03872","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055634072","display_name":"Mao V. Ngo","orcid":"https://orcid.org/0000-0002-4574-4586"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Mao V. Ngo","raw_affiliation_strings":["Singapore University of Technology and Design, Singapore"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design, Singapore","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049199248","display_name":"Tie Luo","orcid":"https://orcid.org/0000-0003-2947-3111"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tie Luo","raw_affiliation_strings":["Missouri University of Science and Technology, Rolla, MO, USA","Missouri University of Science and Technology, Rolla, MO USA"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology, Rolla, MO, USA","institution_ids":["https://openalex.org/I20382870"]},{"raw_affiliation_string":"Missouri University of Science and Technology, Rolla, MO USA","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030858163","display_name":"Tony Q. S. Quek","orcid":"https://orcid.org/0000-0002-4037-3149"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tony Q. S. Quek","raw_affiliation_strings":["Singapore University of Technology and Design, Singapore"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design, Singapore","institution_ids":["https://openalex.org/I152815399"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055634072"],"corresponding_institution_ids":["https://openalex.org/I152815399"],"apc_list":null,"apc_paid":null,"fwci":0.1398,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55221577,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"3","issue":"1","first_page":"1","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8320175409317017},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6612357497215271},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5984712243080139},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5541653633117676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5266844034194946},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.4816819131374359},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.4657982289791107},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4555150866508484},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.45543915033340454},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.4436694085597992},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4233930706977844},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4231034517288208},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38407236337661743},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.2160991132259369},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08788013458251953}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8320175409317017},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6612357497215271},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5984712243080139},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5541653633117676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5266844034194946},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.4816819131374359},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4657982289791107},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4555150866508484},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.45543915033340454},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.4436694085597992},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4233930706977844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4231034517288208},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38407236337661743},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2160991132259369},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08788013458251953},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3480172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480172","pdf_url":null,"source":{"id":"https://openalex.org/S4210175912","display_name":"ACM Transactions on Internet of Things","issn_l":"2577-6207","issn":["2577-6207","2691-1914"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Internet of Things","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2108.03872","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.03872","pdf_url":"https://arxiv.org/pdf/2108.03872","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"mag:3191769047","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2108.03872.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2108.03872","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2108.03872","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2108.03872","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.03872","pdf_url":"https://arxiv.org/pdf/2108.03872","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3191769047.pdf","grobid_xml":"https://content.openalex.org/works/W3191769047.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1821462560","https://openalex.org/W2026493302","https://openalex.org/W2105510466","https://openalex.org/W2119144962","https://openalex.org/W2119717200","https://openalex.org/W2130942839","https://openalex.org/W2155027007","https://openalex.org/W2474046725","https://openalex.org/W2605258629","https://openalex.org/W2752037867","https://openalex.org/W2766304518","https://openalex.org/W2767041744","https://openalex.org/W2786070938","https://openalex.org/W2797932837","https://openalex.org/W2898485069","https://openalex.org/W2902455138","https://openalex.org/W2910068345","https://openalex.org/W2912654452","https://openalex.org/W2950361482","https://openalex.org/W2962677625","https://openalex.org/W2962883027","https://openalex.org/W2962944050","https://openalex.org/W2964248614","https://openalex.org/W2964299589","https://openalex.org/W2995429865","https://openalex.org/W3000049214","https://openalex.org/W3016644709","https://openalex.org/W3083405908","https://openalex.org/W3106312933","https://openalex.org/W3122980409","https://openalex.org/W4236099117","https://openalex.org/W4252642235","https://openalex.org/W6638523607"],"related_works":["https://openalex.org/W3200511797","https://openalex.org/W1839697241","https://openalex.org/W3204616036","https://openalex.org/W3096315052","https://openalex.org/W2968558963","https://openalex.org/W3205027275","https://openalex.org/W3069398144","https://openalex.org/W2114148599","https://openalex.org/W3049216025","https://openalex.org/W3080925004","https://openalex.org/W3187158089","https://openalex.org/W3089361613","https://openalex.org/W2952333754","https://openalex.org/W3166954785","https://openalex.org/W3192637288","https://openalex.org/W2969336389","https://openalex.org/W3135536497","https://openalex.org/W3113015876","https://openalex.org/W2742275837","https://openalex.org/W3020882500"],"abstract_inverted_index":{"The":[0],"advances":[1],"in":[2,15],"deep":[3],"neural":[4],"networks":[5],"(DNN)":[6],"have":[7],"significantly":[8],"enhanced":[9],"real-time":[10],"detection":[11,64,77],"of":[12,42,86,134],"anomalous":[13],"data":[14],"IoT":[16,31,144,158],"applications.":[17],"However,":[18],"the":[19,36,40,44,47,128,172,177,182,188,197],"complexity-accuracy-delay":[20],"dilemma":[21],"persists:":[22],"Complex":[23],"DNN":[24,78],"models":[25,79],"offer":[26],"higher":[27],"accuracy,":[28],"but":[29],"typical":[30],"devices":[32,145],"can":[33],"barely":[34],"afford":[35],"computation":[37],"load,":[38],"and":[39,83,108,146,148,156,165,180,185],"remedy":[41],"offloading":[43],"load":[45],"to":[46,88,126],"cloud":[48],"incurs":[49],"long":[50],"delay.":[51],"In":[52,160],"this":[53,57],"article,":[54],"we":[55,72,94],"address":[56],"challenge":[58],"by":[59,110,131],"proposing":[60],"an":[61,96,139],"adaptive":[62,97,169],"anomaly":[63,76],"scheme":[65,100],"with":[66,80,153,162,191],"hierarchical":[67],"edge":[68],"computing":[69],"(HEC).":[70],"Specifically,":[71],"first":[73],"construct":[74],"multiple":[75],"increasing":[81],"complexity":[82],"associate":[84],"each":[85],"them":[87],"a":[89,105,112,121],"corresponding":[90],"HEC":[91,140],"layer.":[92],"Then,":[93],"design":[95],"model":[98],"selection":[99],"that":[101],"is":[102],"formulated":[103],"as":[104],"contextual-bandit":[106,151],"problem":[107],"solved":[109],"using":[111,142],"reinforcement":[113],"learning":[114],"policy":[115,123],"network":[116],".":[117],"We":[118,137],"also":[119],"incorporate":[120],"parallelism":[122],"training":[124,129],"method":[125],"accelerate":[127],"process":[130],"taking":[132],"advantage":[133],"distributed":[135],"models.":[136],"build":[138],"testbed":[141],"real":[143],"implement":[147],"evaluate":[149],"our":[150,168],"approach":[152,170],"both":[154,163],"univariate":[155,178],"multivariate":[157,189],"datasets.":[159],"comparison":[161],"baseline":[164],"state-of-the-art":[166],"schemes,":[167],"strikes":[171],"best":[173,183,198],"accuracy-delay":[174],"tradeoff":[175],"on":[176,187],"dataset":[179,190],"achieves":[181],"accuracy":[184],"F1-score":[186],"only":[192],"negligibly":[193],"longer":[194],"delay":[195],"than":[196],"(but":[199],"inflexible)":[200],"scheme.":[201]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}