{"id":"https://openalex.org/W2972210858","doi":"https://doi.org/10.1109/sahcn.2019.8824861","title":"Online Data Quality Learning for Quality-Aware Crowdsensing","display_name":"Online Data Quality Learning for Quality-Aware Crowdsensing","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2972210858","doi":"https://doi.org/10.1109/sahcn.2019.8824861","mag":"2972210858"},"language":"en","primary_location":{"id":"doi:10.1109/sahcn.2019.8824861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sahcn.2019.8824861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","raw_type":"proceedings-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/A5100362465","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0003-2138-4608"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Auburn University, Auburn"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Auburn University, Auburn","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042081570","display_name":"Xiaowen Gong","orcid":"https://orcid.org/0000-0001-5124-7941"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaowen Gong","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Auburn University, Auburn"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Auburn University, Auburn","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100362465"],"corresponding_institution_ids":["https://openalex.org/I82497590"],"apc_list":null,"apc_paid":null,"fwci":1.2037,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.83327156,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"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":1.0,"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/T11182","display_name":"Auction Theory and Applications","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9846000075340271,"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/crowdsensing","display_name":"Crowdsensing","score":0.8176755905151367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8168579339981079},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.711129903793335},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.641916811466217},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5984591245651245},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.5655035376548767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4185110032558441},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33252161741256714},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.17269116640090942}],"concepts":[{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.8176755905151367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8168579339981079},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.711129903793335},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.641916811466217},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5984591245651245},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.5655035376548767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4185110032558441},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33252161741256714},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.17269116640090942},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sahcn.2019.8824861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sahcn.2019.8824861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1541505110","https://openalex.org/W1989135160","https://openalex.org/W2005406065","https://openalex.org/W2029327121","https://openalex.org/W2058720102","https://openalex.org/W2085773652","https://openalex.org/W2120826095","https://openalex.org/W2120898858","https://openalex.org/W2127347346","https://openalex.org/W2127963147","https://openalex.org/W2133481610","https://openalex.org/W2134305421","https://openalex.org/W2140890285","https://openalex.org/W2158751658","https://openalex.org/W2160448933","https://openalex.org/W2247944537","https://openalex.org/W2317700292","https://openalex.org/W2398690976","https://openalex.org/W2467851685","https://openalex.org/W2499002200","https://openalex.org/W2513560644","https://openalex.org/W2554839354","https://openalex.org/W2558886717","https://openalex.org/W2574161003","https://openalex.org/W2612206112","https://openalex.org/W2728944333","https://openalex.org/W2732159262","https://openalex.org/W2808820828","https://openalex.org/W2952598059","https://openalex.org/W2963246254","https://openalex.org/W2963301691","https://openalex.org/W4297752578","https://openalex.org/W6634664527","https://openalex.org/W6638921482","https://openalex.org/W6665378013","https://openalex.org/W6679679788","https://openalex.org/W6679959949","https://openalex.org/W6680834967","https://openalex.org/W6691725051","https://openalex.org/W6692768274","https://openalex.org/W6712662495","https://openalex.org/W6726193151"],"related_works":["https://openalex.org/W2971351794","https://openalex.org/W4376155396","https://openalex.org/W1947085858","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W2101991911","https://openalex.org/W2155070487","https://openalex.org/W4311589891","https://openalex.org/W3123835761","https://openalex.org/W2544640472"],"abstract_inverted_index":{"Crowdsensing":[0],"has":[1],"found":[2],"a":[3,17,46,129,220],"variety":[4],"of":[5,16,21,28,38,53,64,102,109,147,158,195,225,262],"applications":[6],"(e.g.,":[7],"spectrum":[8],"sensing,":[9],"environmental":[10],"monitoring)":[11],"by":[12,41],"leveraging":[13],"the":[14,36,42,51,62,70,81,98,107,116,144,153,159,180,189,192,196,212,223,229,237,241,249,254,260,263],"\"wisdom\"":[15],"potentially":[18],"large":[19],"crowd":[20],"mobile":[22],"users":[23],"as":[24,177],"\"workers\".":[25],"The":[26],"value":[27,99],"data":[29,39,54,76,89,103,111,138,145,151,168],"collected":[30],"in":[31,45,104,222],"crowdsensing":[32,47,71,94,132],"heavily":[33],"depends":[34],"on":[35,79,152,236],"quality":[37,52,108,139,146,161],"provided":[40],"workers":[43,86,148],"participating":[44],"task.":[48],"In":[49,125],"general,":[50],"varies":[55],"for":[56,69,179,239,247],"different":[57],"workers.":[58,91],"To":[59],"fully":[60],"exploit":[61],"potential":[63],"crowdsensing,":[65],"it":[66],"is":[67,112,199],"important":[68],"requester":[72,82,117],"to":[73,85,115,163,171,187,211,228],"know":[74],"workers'":[75,110,121],"quality,":[77],"based":[78],"which":[80],"allocates":[83],"tasks":[84],"and":[87,100,167,218,244,246,253],"aggregates":[88],"from":[90,149],"Such":[92],"quality-aware":[93],"can":[95],"greatly":[96],"improve":[97],"usefulness":[101],"crowdsensing.":[105],"However,":[106],"often":[113],"unknown":[114],"(due":[118],"to,":[119],"e.g.,":[120],"characteristics":[122],"are":[123],"unknown).":[124],"this":[126],"paper,":[127],"under":[128,204],"dynamic":[130],"multi-task":[131],"framework,":[133],"we":[134],"devise":[135],"an":[136],"online":[137,173],"learning":[140,174],"algorithm":[141,185,209,264],"that":[142,191,203],"learns":[143],"their":[150],"fly,":[154],"while":[155],"making":[156],"use":[157],"learned":[160],"information":[162],"perform":[164],"task":[165],"allocation":[166],"aggregation.":[169],"Compared":[170],"prior":[172],"algorithms":[175],"(such":[176],"those":[178],"multi-armed":[181],"bandit":[182],"problems),":[183],"our":[184,208],"needs":[186],"overcome":[188],"challenge":[190],"ground":[193],"truth":[194],"interested":[197],"variable":[198],"unknown.":[200],"We":[201,233,258],"show":[202],"some":[205],"mild":[206],"conditions,":[207],"converges":[210],"offline":[213,230],"optimal":[214,231],"strategy":[215],"over":[216],"time,":[217],"have":[219],"regret":[221,238],"order":[224],"O(logt)":[226],"compared":[227],"strategy.":[232],"provide":[234],"bounds":[235],"both":[240,248],"requester's":[242],"utility":[243],"cost,":[245],"simple":[250],"average":[251,256],"rule":[252],"weighted":[255],"rule.":[257],"demonstrate":[259],"efficiency":[261],"using":[265],"simulation":[266],"results.":[267]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
