{"id":"https://openalex.org/W4399324049","doi":"https://doi.org/10.1145/3643832.3661888","title":"CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference","display_name":"CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference","publication_year":2024,"publication_date":"2024-06-03","ids":{"openalex":"https://openalex.org/W4399324049","doi":"https://doi.org/10.1145/3643832.3661888"},"language":"en","primary_location":{"id":"doi:10.1145/3643832.3661888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661888","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661888","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 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661888","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099012557","display_name":"Mohammad Mehdi Rastikerdar","orcid":"https://orcid.org/0000-0002-7643-7722"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Mehdi Rastikerdar","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-7643-7722","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, United States of America","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100708600","display_name":"Jin Huang","orcid":"https://orcid.org/0009-0007-2118-2834"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin Huang","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, United States of America"],"raw_orcid":"https://orcid.org/0009-0007-2118-2834","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, United States of America","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014286716","display_name":"Shiwei Fang","orcid":"https://orcid.org/0000-0003-0134-5003"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiwei Fang","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, United States of America"],"raw_orcid":"https://orcid.org/0000-0003-0134-5003","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, United States of America","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085489377","display_name":"Hui Guan","orcid":"https://orcid.org/0000-0001-9128-2231"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Guan","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-9128-2231","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, United States of America","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074602782","display_name":"Deepak Ganesan","orcid":"https://orcid.org/0000-0003-2762-9194"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepak Ganesan","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, United States of America"],"raw_orcid":"https://orcid.org/0000-0003-2762-9194","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, United States of America","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8749,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73615171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"505","last_page":"518"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9914000034332275,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9908000230789185,"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":0.747005820274353},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7311072945594788},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6506133675575256},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.6203647255897522},{"id":"https://openalex.org/keywords/cactus","display_name":"Cactus","score":0.514612078666687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4392586946487427},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42261913418769836},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.23171555995941162},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.05306330323219299}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.747005820274353},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7311072945594788},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6506133675575256},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.6203647255897522},{"id":"https://openalex.org/C2780106946","wikidata":"https://www.wikidata.org/wiki/Q14560","display_name":"Cactus","level":2,"score":0.514612078666687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392586946487427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42261913418769836},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.23171555995941162},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.05306330323219299},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3643832.3661888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661888","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661888","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 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3643832.3661888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661888","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661888","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 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1226108236","display_name":null,"funder_award_id":"DMS-2220211","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G13649008","display_name":null,"funder_award_id":"CNS-2224054","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1637379582","display_name":null,"funder_award_id":"W911NF-17-2-0196","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G1710177955","display_name":null,"funder_award_id":"CNS- 2338512","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4451185058","display_name":null,"funder_award_id":"P30AG073107","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G4672264128","display_name":null,"funder_award_id":"CNS-2312396","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399324049.pdf"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1997430507","https://openalex.org/W2118858186","https://openalex.org/W2402040300","https://openalex.org/W2468875367","https://openalex.org/W2531327146","https://openalex.org/W2546536770","https://openalex.org/W2550742086","https://openalex.org/W2556833785","https://openalex.org/W2734941459","https://openalex.org/W2752236330","https://openalex.org/W2784041417","https://openalex.org/W2804032941","https://openalex.org/W2806471870","https://openalex.org/W2860338957","https://openalex.org/W2886851211","https://openalex.org/W2887117815","https://openalex.org/W2891018693","https://openalex.org/W2894954915","https://openalex.org/W2900071409","https://openalex.org/W2910489404","https://openalex.org/W2944701285","https://openalex.org/W2946948417","https://openalex.org/W2962677625","https://openalex.org/W2962746461","https://openalex.org/W2962883027","https://openalex.org/W2962965870","https://openalex.org/W2963122961","https://openalex.org/W2963770864","https://openalex.org/W2964059111","https://openalex.org/W2969940440","https://openalex.org/W2984618279","https://openalex.org/W2990138404","https://openalex.org/W3034340181","https://openalex.org/W3034755948","https://openalex.org/W3081141044","https://openalex.org/W3088405768","https://openalex.org/W3094466514","https://openalex.org/W3128506819","https://openalex.org/W3130517342","https://openalex.org/W3181469834","https://openalex.org/W3183048323","https://openalex.org/W3215698464","https://openalex.org/W4287591828","https://openalex.org/W4288259332","https://openalex.org/W4289305285","https://openalex.org/W4383754252","https://openalex.org/W6726275242","https://openalex.org/W6801461832"],"related_works":["https://openalex.org/W1009021000","https://openalex.org/W2095439288","https://openalex.org/W2955852887","https://openalex.org/W3133003094","https://openalex.org/W2963119989","https://openalex.org/W3109976485","https://openalex.org/W1979575543","https://openalex.org/W4226056732","https://openalex.org/W2747450282","https://openalex.org/W1511099535"],"abstract_inverted_index":{"While":[0],"existing":[1],"strategies":[2],"to":[3,33,73,92],"execute":[4],"deep":[5],"learning-based":[6],"classification":[7,31,62],"on":[8,16],"low-power":[9],"platforms":[10],"assume":[11],"the":[12,34,74,88,102],"models":[13],"are":[14],"trained":[15],"all":[17],"classes":[18,71],"of":[19,39,70,105,144],"interest,":[20],"this":[21],"paper":[22],"posits":[23],"that":[24,130],"adopting":[25],"context-awareness":[26],"i.e.":[27],"narrowing":[28],"down":[29],"a":[30,53,64,67,83,142],"task":[32],"current":[35,75],"deployment":[36],"context":[37,76,79,116],"consisting":[38],"only":[40],"recent":[41],"inference":[42],"queries":[43],"can":[44],"substantially":[45],"enhance":[46],"performance":[47,120],"in":[48,135],"resource-constrained":[49],"environments.":[50],"We":[51,128],"propose":[52],"new":[54,84],"paradigm,":[55],"CACTUS,":[56],"for":[57],"scalable":[58],"and":[59,114,119,138,146],"efficient":[60],"context-aware":[61,106,110],"where":[63],"micro-classifier":[65],"recognizes":[66],"small":[68],"set":[69],"relevant":[72],"and,":[77],"when":[78],"change":[80],"happens":[81],"(e.g.,":[82],"class":[85],"comes":[86],"into":[87],"scene),":[89],"rapidly":[90],"switches":[91],"another":[93],"suitable":[94],"micro-classifier.":[95],"CACTUS":[96,131],"features":[97],"several":[98],"innovations,":[99],"including":[100],"optimizing":[101],"training":[103],"cost":[104],"classifiers,":[107,113],"enabling":[108],"on-the-fly":[109],"switching":[111,117,126],"between":[112],"balancing":[115],"costs":[118],"gains":[121],"via":[122],"simple":[123],"yet":[124],"effective":[125],"policies.":[127],"show":[129],"achieves":[132],"significant":[133],"benefits":[134],"accuracy,":[136],"latency,":[137],"compute":[139],"budget":[140],"across":[141],"range":[143],"datasets":[145],"IoT":[147],"platforms.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}