{"id":"https://openalex.org/W4405974464","doi":"https://doi.org/10.1109/sec62691.2024.00013","title":"CroMA: Enhancing Fault-Resilience of Machine Learning-Coupled IoT Applications","display_name":"CroMA: Enhancing Fault-Resilience of Machine Learning-Coupled IoT Applications","publication_year":2024,"publication_date":"2024-12-04","ids":{"openalex":"https://openalex.org/W4405974464","doi":"https://doi.org/10.1109/sec62691.2024.00013"},"language":"en","primary_location":{"id":"doi:10.1109/sec62691.2024.00013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sec62691.2024.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACM Symposium on Edge Computing (SEC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5075956952","display_name":"Yousef AlShehri","orcid":null},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yousef AlShehri","raw_affiliation_strings":["School of Computing, University of Georgia,Athens,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, University of Georgia,Athens,United States","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082188602","display_name":"Lakshmish Ramaswamy","orcid":"https://orcid.org/0000-0002-4567-4186"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lakshmish Ramaswamy","raw_affiliation_strings":["School of Computing, University of Georgia,Athens,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, University of Georgia,Athens,United States","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":0.3556,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60683153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"59","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9951000213623047,"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":0.9951000213623047,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.979200005531311,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.9509999752044678,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/resilience","display_name":"Resilience (materials science)","score":0.805864691734314},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7118426561355591},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.6400562524795532},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.46909770369529724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33014971017837524},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.2706349492073059},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06029602885246277},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.0581565797328949}],"concepts":[{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.805864691734314},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7118426561355591},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.6400562524795532},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.46909770369529724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33014971017837524},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2706349492073059},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06029602885246277},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0581565797328949},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sec62691.2024.00013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sec62691.2024.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACM Symposium on Edge Computing (SEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.47999998927116394}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2022851810","https://openalex.org/W2025768430","https://openalex.org/W2150015649","https://openalex.org/W2326925005","https://openalex.org/W2896457183","https://openalex.org/W2898352493","https://openalex.org/W2919115771","https://openalex.org/W2945477047","https://openalex.org/W2950635152","https://openalex.org/W2997591727","https://openalex.org/W3110297359","https://openalex.org/W3112540535","https://openalex.org/W3160131050","https://openalex.org/W3208956771","https://openalex.org/W4205930708","https://openalex.org/W4213447070","https://openalex.org/W4285404854","https://openalex.org/W4313156423","https://openalex.org/W4360764814","https://openalex.org/W4366308323","https://openalex.org/W4393024955","https://openalex.org/W4403421464","https://openalex.org/W6675401909","https://openalex.org/W6778883912","https://openalex.org/W6779879114","https://openalex.org/W6849338249","https://openalex.org/W6861995665"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4245926026","https://openalex.org/W4311097251","https://openalex.org/W2586548817","https://openalex.org/W2390279801","https://openalex.org/W2625093826","https://openalex.org/W4391913857","https://openalex.org/W2358668433"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"(ML)-coupled":[2],"Internet":[3],"of":[4,46,83,123,142,162,230,236,257,265],"Things":[5],"(IoT)":[6],"applications":[7,49],"are":[8,101,112],"becoming":[9],"increasingly":[10],"popular":[11],"in":[12,29,147,262],"many":[13,67],"domains.":[14],"However,":[15],"faulty":[16,55,63,74,86,114],"sensor":[17,56,64,118,195,250,266],"readings":[18,65,82,111,178],"pose":[19],"a":[20,135,153,185,234],"significant":[21],"challenge":[22],"to":[23,33,61,72,116,166,173,175,188,217],"IoT-ML":[24],"applications.":[25],"Noise":[26],"is":[27,145,150],"inherent":[28],"IoT":[30,38,259],"environments":[31,39],"due":[32],"issues":[34],"such":[35],"as":[36,204],"harsh":[37],"and":[40,69,85,98,198,221],"hardware":[41],"limitations.":[42],"Unfortunately,":[43,103],"the":[44,47,77,121,171,180,228,231,254,258,263],"performance":[45],"ML":[48,128],"quickly":[50],"degrades":[51],"when":[52,108],"faced":[53],"with":[54],"readings.":[57,168,225],"Most":[58],"current":[59],"techniques":[60,105],"handle":[62],"require":[66],"models,":[68],"they":[70],"attempt":[71],"correct":[73],"data":[75],"using":[76],"entire":[78],"sample":[79],"containing":[80],"both":[81],"non-faulty":[84],"sensors,":[87],"making":[88],"them":[89],"unsuitable":[90],"for":[91,138],"edge":[92,126],"ML-IoT":[93],"applications,":[94,129],"where":[95],"real-time":[96],"processing":[97],"low":[99],"latency":[100],"critical.":[102],"these":[104],"fail,":[106],"especially":[107],"multiple":[109],"sensors'":[110,167,177,224],"concurrently":[113],"(due":[115],"simultaneous":[117,140],"failures).":[119],"With":[120],"goal":[122],"building":[124],"robust":[125],"IoT-coupled":[127],"this":[130],"paper":[131],"proposes":[132],"CroMA":[133,144,183,232,247],"-":[134],"proactive":[136],"approach":[137],"overcoming":[139],"failure":[141],"sensors.":[143],"unique":[146],"that":[148,246],"it":[149],"based":[151],"upon":[152],"masked":[154,160],"autoencoder":[155,172],"trained":[156],"on":[157],"several":[158],"randomly":[159],"portions":[161],"training":[163],"samples,":[164],"corresponding":[165],"This":[169],"enables":[170],"learn":[174],"predict":[176],"through":[179,233],"unmasked":[181],"portions.":[182],"incorporates":[184],"novel":[186],"technique":[187],"optimize":[189],"its":[190],"prediction":[191],"accuracy":[192,256],"by":[193],"leveraging":[194],"correlations-based":[196],"masking":[197],"identically":[199],"treats":[200],"all":[201],"fault":[202],"types":[203],"one":[205],"type":[206],"via":[207],"masking.":[208],"CroMA's":[209],"network":[210],"design":[211],"includes":[212],"gated":[213],"recurrent":[214],"units":[215],"(GRUs)":[216],"capture":[218],"long-short-term":[219],"dependencies":[220],"correlations":[222],"between":[223],"We":[226],"confirm":[227],"effectiveness":[229],"series":[235],"experiments":[237],"involving":[238],"three":[239],"distinct":[240],"datasets.":[241],"The":[242],"experimental":[243],"results":[244],"affirm":[245],"effectively":[248],"corrects":[249],"faults,":[251],"thereby":[252],"preserving":[253],"classification":[255],"ML-based":[260],"application":[261],"face":[264],"failures.":[267]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}