{"id":"https://openalex.org/W3154489659","doi":"https://doi.org/10.1109/jiot.2021.3073705","title":"Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data","display_name":"Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data","publication_year":2021,"publication_date":"2021-04-16","ids":{"openalex":"https://openalex.org/W3154489659","doi":"https://doi.org/10.1109/jiot.2021.3073705","mag":"3154489659"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2021.3073705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2021.3073705","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":null,"license_id":null,"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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023833145","display_name":"Thien-Binh Dang","orcid":"https://orcid.org/0000-0002-5168-2537"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Thien-Binh Dang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5168-2537","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041230893","display_name":"Duc-Tai Le","orcid":"https://orcid.org/0000-0002-5286-6629"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Duc-Tai Le","raw_affiliation_strings":["College of Computing, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5286-6629","affiliations":[{"raw_affiliation_string":"College of Computing, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100709069","display_name":"Ti\u1ebfn D\u0169ng Nguy\u1ec5n","orcid":"https://orcid.org/0000-0003-0064-4044"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tien-Dung Nguyen","raw_affiliation_strings":["Convergence Research Center, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0064-4044","affiliations":[{"raw_affiliation_string":"Convergence Research Center, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026687678","display_name":"Moonseong Kim","orcid":"https://orcid.org/0000-0003-2692-6883"},"institutions":[{"id":"https://openalex.org/I5324124","display_name":"Seoul Theological University","ror":"https://ror.org/00m4aws33","country_code":"KR","type":"education","lineage":["https://openalex.org/I5324124"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Moonseong Kim","raw_affiliation_strings":["Department of IT Convergence Software, Seoul Theological University, Bucheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2692-6883","affiliations":[{"raw_affiliation_string":"Department of IT Convergence Software, Seoul Theological University, Bucheon, South Korea","institution_ids":["https://openalex.org/I5324124"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054933494","display_name":"Hyunseung Choo","orcid":"https://orcid.org/0000-0002-6485-3155"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunseung Choo","raw_affiliation_strings":["College of Computing, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-6485-3155","affiliations":[{"raw_affiliation_string":"College of Computing, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5188,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.90958814,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"8","issue":"20","first_page":"15468","last_page":"15485"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9980999827384949,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/overfitting","display_name":"Overfitting","score":0.8266791701316833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7481845021247864},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7437362670898438},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5616048574447632},{"id":"https://openalex.org/keywords/monotone-polygon","display_name":"Monotone polygon","score":0.5192378163337708},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.4638751745223999},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4533993899822235},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42986243963241577},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42398595809936523},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4104931652545929},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38295915722846985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3184557557106018},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1994934380054474},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1036805808544159}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8266791701316833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7481845021247864},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7437362670898438},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5616048574447632},{"id":"https://openalex.org/C2834757","wikidata":"https://www.wikidata.org/wiki/Q4925424","display_name":"Monotone polygon","level":2,"score":0.5192378163337708},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.4638751745223999},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4533993899822235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42986243963241577},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42398595809936523},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4104931652545929},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38295915722846985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3184557557106018},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1994934380054474},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1036805808544159},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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":1,"locations":[{"id":"doi:10.1109/jiot.2021.3073705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2021.3073705","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2587005943","display_name":null,"funder_award_id":"NRF-2020R1A2C2008447","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1664828614","https://openalex.org/W1986830331","https://openalex.org/W1992380169","https://openalex.org/W1993694278","https://openalex.org/W1999935041","https://openalex.org/W2055865901","https://openalex.org/W2056563033","https://openalex.org/W2088149543","https://openalex.org/W2102981928","https://openalex.org/W2117178121","https://openalex.org/W2120787649","https://openalex.org/W2142355131","https://openalex.org/W2150015649","https://openalex.org/W2151764894","https://openalex.org/W2262340115","https://openalex.org/W2294798173","https://openalex.org/W2470039937","https://openalex.org/W2499741707","https://openalex.org/W2509223312","https://openalex.org/W2531448500","https://openalex.org/W2547414809","https://openalex.org/W2793988933","https://openalex.org/W2887731362","https://openalex.org/W2907421153","https://openalex.org/W2913865526","https://openalex.org/W2915969651","https://openalex.org/W2933589034","https://openalex.org/W2942478455","https://openalex.org/W2953074371","https://openalex.org/W2961099251","https://openalex.org/W2964097029","https://openalex.org/W2991919059","https://openalex.org/W3010641675","https://openalex.org/W3106312933","https://openalex.org/W6675780863","https://openalex.org/W6677193781"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2143820878","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,18,86,133,193],"is":[2,196],"essential":[3],"to":[4,45,60,77,99,115,147,181,198],"guarantee":[5],"the":[6,25,54,67,74,79,84,90,101,116,131,137,157],"correctness":[7],"of":[8,14,24,29,118,122,139],"sensory":[9,63],"data":[10,69,163],"collected":[11,68],"from":[12],"Internet":[13],"Things.":[15],"The":[16,50],"latest":[17],"approaches":[19],"only":[20,187],"operate":[21],"under":[22],"one":[23],"two":[26],"general":[27,97,112,144],"forms":[28],"short-term":[30,150],"or":[31],"long-term":[32,152],"anomaly.":[33],"In":[34],"this":[35],"article,":[36],"we":[37,93,141],"propose":[38],"monotone":[39,109],"split":[40,110],"and":[41,111,125,151,173],"conquer":[42],"(MSC)":[43],"scheme":[44,52],"tackle":[46],"both":[47],"anomaly":[48,145,192],"forms.":[49],"proposed":[51],"exploits":[53],"spatial\u2013temporal":[55],"correlation":[56],"between":[57],"neighboring":[58],"sensors":[59],"detect":[61],"abnormal":[62],"data.":[64],"MSC":[65,119,167,185],"splits":[66],"into":[70],"monotonic":[71],"subtrends":[72],"in":[73,120,130],"training":[75],"phase":[76],"establish":[78],"trend-based":[80],"normal":[81],"profiles":[82],"for":[83,191],"online":[85,132],"phase.":[87,134],"To":[88,135],"eradicate":[89],"overfitting":[91],"phenomenon,":[92],"further":[94],"develop":[95],"a":[96,143,188],"formulation":[98,113],"estimate":[100],"square":[102],"prediction":[103],"error":[104],"(SPE)":[105],"control":[106],"limit.":[107],"Both":[108],"contribute":[114],"advancement":[117],"terms":[121],"accuracy":[123],"(ACC)":[124],"false":[126],"positive":[127],"rate":[128],"(FPR)":[129],"evaluate":[136],"performance":[138],"MSC,":[140],"design":[142],"model":[146],"generate":[148],"artificial":[149],"anomalies.":[153],"Numerical":[154],"experiments":[155],"with":[156],"Intel":[158],"Berkeley":[159],"Research":[160],"Lab":[161],"(IBRL)":[162],"set":[164],"demonstrate":[165],"that":[166],"obtains":[168],"about":[169],"8%":[170],"higher":[171],"ACC":[172],"5%":[174],"lower":[175],"FPR":[176],"on":[177],"average":[178],"when":[179],"compared":[180],"existing":[182],"schemes.":[183],"Remarkably,":[184],"requires":[186],"few":[189],"observations":[190],"as":[194],"it":[195],"applicable":[197],"real-time":[199],"systems.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
