{"id":"https://openalex.org/W4416956242","doi":"https://doi.org/10.1145/3769102.3774634","title":"A Cost-Aware Hierarchical Cascade for Anomaly Detection at the Edge in Connected Vehicles","display_name":"A Cost-Aware Hierarchical Cascade for Anomaly Detection at the Edge in Connected Vehicles","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4416956242","doi":"https://doi.org/10.1145/3769102.3774634"},"language":null,"primary_location":{"id":"doi:10.1145/3769102.3774634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769102.3774634","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769102.3774634","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3769102.3774634","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111005302","display_name":"Cheng-Hsun Chang","orcid":"https://orcid.org/0009-0002-0609-3490"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Cheng-Hsun Chang","raw_affiliation_strings":["Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0009-0002-0609-3490","affiliations":[{"raw_affiliation_string":"Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012736869","display_name":"Adarsh Prasad Behera","orcid":"https://orcid.org/0000-0001-7220-5353"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Adarsh Prasad Behera","raw_affiliation_strings":["Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-7220-5353","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051224517","display_name":"Sami Pettersson","orcid":"https://orcid.org/0009-0008-4530-3378"},"institutions":[{"id":"https://openalex.org/I4210145666","display_name":"Embedded Systems (United States)","ror":"https://ror.org/04742eh45","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sophia Zhang Pettersson","raw_affiliation_strings":["Cloud and Embedded Platform, Traton AB, S\u00f6dert\u00e4lje, Sweden"],"raw_orcid":"https://orcid.org/0009-0008-4530-3378","affiliations":[{"raw_affiliation_string":"Cloud and Embedded Platform, Traton AB, S\u00f6dert\u00e4lje, Sweden","institution_ids":["https://openalex.org/I4210145666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037402766","display_name":"James Gross","orcid":"https://orcid.org/0000-0001-6682-6559"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"James Gross","raw_affiliation_strings":["Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-6682-6559","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7985000014305115,"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":0.7985000014305115,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.03359999880194664,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.023000000044703484,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/anomaly-detection","display_name":"Anomaly detection","score":0.7391999959945679},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6308000087738037},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.551800012588501},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.527999997138977},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.47510001063346863},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4068000018596649},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4032000005245209},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.40119999647140503},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.36570000648498535},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.3391000032424927}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7391999959945679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7020000219345093},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6308000087738037},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.551800012588501},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.527999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47760000824928284},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39660000801086426},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.36570000648498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3345000147819519},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C2778156585","wikidata":"https://www.wikidata.org/wiki/Q174053","display_name":"Relay","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26919999718666077},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.262800008058548},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3769102.3774634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769102.3774634","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769102.3774634","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3769102.3774634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769102.3774634","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769102.3774634","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416956242.pdf","grobid_xml":"https://content.openalex.org/works/W4416956242.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2164598857","https://openalex.org/W2319759828","https://openalex.org/W2945434604","https://openalex.org/W2949848919","https://openalex.org/W2950361482","https://openalex.org/W2962677625","https://openalex.org/W3089028909","https://openalex.org/W3166166117","https://openalex.org/W4300672471","https://openalex.org/W4318486034","https://openalex.org/W4381489698","https://openalex.org/W4382203413","https://openalex.org/W4387227815","https://openalex.org/W4402042247","https://openalex.org/W4408363154"],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1],"anomaly":[2,154],"detection":[3,110,155],"(TSAD)":[4],"is":[5,23],"essential":[6],"for":[7,45,66],"ensuring":[8],"the":[9,79,89,124],"safety":[10],"and":[11,15,30,81,109,151],"reliability":[12],"of":[13,71,131],"intelligent":[14,158],"autonomous":[16,119],"vehicles.":[17],"In":[18],"edge-cloud":[19],"systems,":[20],"this":[21,58],"task":[22],"challenging":[24],"due":[25],"to":[26,103],"limited":[27],"on-board":[28],"resources":[29],"real-time":[31,153],"constraints.":[32],"Deep":[33],"learning":[34],"(DL)":[35],"models":[36,50],"offer":[37],"high":[38],"accuracy":[39,128],"but":[40,53],"are":[41,51],"too":[42],"computationally":[43],"demanding":[44],"embedded":[46],"devices,":[47],"whereas":[48],"lightweight":[49,73],"efficient":[52,152],"less":[54],"precise.":[55],"To":[56],"address":[57],"trade-off,":[59],"we":[60],"propose":[61],"a":[62,82,132],"hierarchical":[63,147],"cascaded":[64],"framework":[65],"unsupervised":[67],"multivariate":[68],"TSAD,":[69],"consisting":[70],"two":[72],"Gaussian":[74],"Mixture":[75],"Models":[76],"(GMMs)":[77],"on":[78,96,113],"edge":[80],"fully":[83],"connected":[84],"Variational":[85],"Autoencoder":[86],"(FC-VAE)":[87],"in":[88,156],"cloud.":[90],"An":[91],"adaptive":[92],"offloading":[93],"mechanism":[94],"based":[95],"online":[97],"regret":[98],"minimization":[99],"dynamically":[100],"decides":[101],"when":[102],"escalate":[104],"inputs,":[105],"balancing":[106],"inference":[107,148],"cost":[108,138],"accuracy.":[111],"Experiments":[112],"real-world":[114],"sensor":[115],"data":[116],"from":[117],"Scania's":[118],"mining":[120],"trucks":[121],"show":[122],"that":[123,145],"proposed":[125],"method":[126],"achieves":[127],"within":[129],"1%":[130],"cloud-only":[133],"FC-VAE":[134],"while":[135],"reducing":[136],"computation":[137],"by":[139],"over":[140],"85%.":[141],"These":[142],"results":[143],"demonstrate":[144],"cost-aware":[146],"enables":[149],"scalable":[150],"edge-centric":[157],"transportation":[159],"systems.":[160]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-03T00:00:00"}
