{"id":"https://openalex.org/W2983071751","doi":"https://doi.org/10.1145/3356250.3360043","title":"A closer look at quality-aware runtime assessment of sensing models in multi-device environments","display_name":"A closer look at quality-aware runtime assessment of sensing models in multi-device environments","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2983071751","doi":"https://doi.org/10.1145/3356250.3360043","mag":"2983071751"},"language":"en","primary_location":{"id":"doi:10.1145/3356250.3360043","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356250.3360043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","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/A5084727763","display_name":"Chulhong Min","orcid":"https://orcid.org/0000-0002-5197-9840"},"institutions":[{"id":"https://openalex.org/I4210098141","display_name":"Nokia (United Kingdom)","ror":"https://ror.org/00zpf0626","country_code":"GB","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I4210098141"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chulhong Min","raw_affiliation_strings":["Nokia Bell Labs, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, UK","institution_ids":["https://openalex.org/I4210098141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101566671","display_name":"Alessandro Montanari","orcid":"https://orcid.org/0000-0003-4444-6242"},"institutions":[{"id":"https://openalex.org/I4210098141","display_name":"Nokia (United Kingdom)","ror":"https://ror.org/00zpf0626","country_code":"GB","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I4210098141"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alessandro Montanari","raw_affiliation_strings":["Nokia Bell Labs, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, UK","institution_ids":["https://openalex.org/I4210098141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020784139","display_name":"Akhil Mathur","orcid":"https://orcid.org/0000-0002-1475-3017"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Akhil Mathur","raw_affiliation_strings":["University College London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College London, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053438231","display_name":"Fahim Kawsar","orcid":"https://orcid.org/0000-0001-5057-9557"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Fahim Kawsar","raw_affiliation_strings":["TU Delft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Delft","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"271","last_page":"284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10914","display_name":"Tactile and Sensory Interactions","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.885719358921051},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6755846738815308},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4291078746318817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3848159909248352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.369829922914505},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.33494076132774353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.885719358921051},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6755846738815308},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4291078746318817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3848159909248352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.369829922914505},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33494076132774353}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3356250.3360043","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356250.3360043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1514211892","https://openalex.org/W1580375566","https://openalex.org/W1598033630","https://openalex.org/W1598741436","https://openalex.org/W1618905105","https://openalex.org/W1985623362","https://openalex.org/W1993158854","https://openalex.org/W1995875735","https://openalex.org/W2012942264","https://openalex.org/W2052666245","https://openalex.org/W2057907879","https://openalex.org/W2068503758","https://openalex.org/W2096482910","https://openalex.org/W2098824882","https://openalex.org/W2104239624","https://openalex.org/W2111825263","https://openalex.org/W2126511896","https://openalex.org/W2137100320","https://openalex.org/W2142057670","https://openalex.org/W2144169341","https://openalex.org/W2149708663","https://openalex.org/W2157130167","https://openalex.org/W2162555816","https://openalex.org/W2254249950","https://openalex.org/W2270470215","https://openalex.org/W2337546824","https://openalex.org/W2340025709","https://openalex.org/W2342792048","https://openalex.org/W2516259218","https://openalex.org/W2548695521","https://openalex.org/W2553915786","https://openalex.org/W2598207902","https://openalex.org/W2607603241","https://openalex.org/W2620664872","https://openalex.org/W2783920628","https://openalex.org/W2786858043","https://openalex.org/W2851629429","https://openalex.org/W2889016544","https://openalex.org/W2896518452","https://openalex.org/W2899387030","https://openalex.org/W2933224119","https://openalex.org/W2963403868","https://openalex.org/W2964212410","https://openalex.org/W2971637278","https://openalex.org/W3101667008","https://openalex.org/W3104328743","https://openalex.org/W4385245566","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0],"increasing":[1],"availability":[2],"of":[3,113,152,206],"multiple":[4,98,183],"sensory":[5,21,30],"devices":[6,99],"on":[7,41,139,167,210],"or":[8],"near":[9],"a":[10,42,44,89,111,161],"human":[11],"body":[12],"has":[13],"opened":[14],"brand":[15],"new":[16],"opportunities":[17],"to":[18,69,73,215],"leverage":[19],"redundant":[20],"signals":[22,36],"for":[23,78,125],"powerful":[24],"sensing":[25,92,102,172],"applications.":[26],"For":[27],"instance,":[28],"personal-scale":[29],"inferences":[31],"with":[32,160],"motion":[33,184],"and":[34,46,58,80,100,150,165,185],"audio":[35,186],"can":[37,174],"be":[38,175],"done":[39],"individually":[40],"smartphone,":[43],"smartwatch,":[45],"even":[47],"an":[48],"earbud":[49],"-":[50],"each":[51,211],"offering":[52],"unique":[53],"sensor":[54],"quality,":[55],"model":[56,114],"accuracy,":[57],"runtime":[59,127,171],"behaviour.":[60],"At":[61],"execution":[62],"time,":[63],"however,":[64],"it":[65],"is":[66,158],"incredibly":[67],"challenging":[68],"assess":[70],"these":[71],"characteristics":[72],"select":[74],"the":[75,107,126,168,203],"best":[76,108],"device":[77,109,200,212],"accurate":[79],"resource-efficient":[81],"inferences.":[82],"To":[83],"this":[84],"end,":[85],"we":[86],"look":[87],"at":[88,116,202],"quality-aware":[90],"collaborative":[91],"system":[93],"that":[94,145,170,189],"actively":[95],"interplays":[96],"across":[97,182],"respective":[101],"models.":[103],"It":[104],"dynamically":[105],"selects":[106],"as":[110,213],"function":[112],"accuracy":[115,197],"any":[117],"given":[118],"context.":[119],"We":[120],"propose":[121],"two":[122],"complementary":[123],"techniques":[124,191],"quality":[128,142,173],"assessment.":[129],"Borrowing":[130],"principles":[131],"from":[132,177],"active":[133],"learning,":[134],"our":[135,190],"first":[136],"technique":[137,157],"runs":[138],"three":[140],"heuristic-based":[141],"assessment":[143],"functions":[144],"employ":[146],"confidence,":[147],"margin":[148],"sampling,":[149],"entropy":[151],"models'":[153],"output.":[154],"Our":[155,180],"second":[156],"built":[159],"siamese":[162],"neural":[163],"network":[164],"acts":[166],"premise":[169],"learned":[176],"historical":[178],"data.":[179],"evaluation":[181],"datasets":[187],"shows":[188],"provide":[192],"12%":[193],"increase":[194],"in":[195],"overall":[196],"through":[198],"dynamic":[199],"selection":[201],"average":[204],"expense":[205],"13":[207],"mW":[208],"power":[209],"compared":[214],"traditional":[216],"single-device":[217],"approaches.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
