{"id":"https://openalex.org/W3168573457","doi":"https://doi.org/10.1145/3447548.3467070","title":"Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning","display_name":"Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3168573457","doi":"https://doi.org/10.1145/3447548.3467070","mag":"3168573457"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467070","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","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/A5076988964","display_name":"Hao Wang","orcid":"https://orcid.org/0009-0004-0199-0488"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102923184","display_name":"Chi Harold Liu","orcid":"https://orcid.org/0000-0002-0252-329X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi Harold Liu","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038171053","display_name":"Zipeng Dai","orcid":"https://orcid.org/0000-0002-2479-9801"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zipeng Dai","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039176528","display_name":"Jian Tang","orcid":"https://orcid.org/0000-0003-4418-0114"},"institutions":[{"id":"https://openalex.org/I4210093653","display_name":"Midea Group (China)","ror":"https://ror.org/006fm2278","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210093653"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Tang","raw_affiliation_strings":["Midea Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Midea Group, Beijing, China","institution_ids":["https://openalex.org/I4210093653"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054991337","display_name":"Guoren Wang","orcid":"https://orcid.org/0000-0002-0181-8379"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoren Wang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076988964"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":6.7999,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.96238954,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3679","last_page":"3687"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9930999875068665,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9832000136375427,"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/computer-science","display_name":"Computer science","score":0.7831863164901733},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7631683349609375},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.7047110795974731},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.6994683742523193},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.6030778884887695},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5867326259613037},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4811733663082123},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.47338977456092834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46504321694374084},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.414778470993042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3612763285636902},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3387479782104492},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2771567106246948},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2508774995803833},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10685557126998901},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08986568450927734}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7831863164901733},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7631683349609375},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.7047110795974731},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.6994683742523193},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6030778884887695},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5867326259613037},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4811733663082123},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.47338977456092834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46504321694374084},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.414778470993042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3612763285636902},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3387479782104492},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2771567106246948},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2508774995803833},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10685557126998901},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08986568450927734},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467070","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G8331661634","display_name":null,"funder_award_id":"62022017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2145339207","https://openalex.org/W2886180905","https://openalex.org/W2922260520","https://openalex.org/W3046988891","https://openalex.org/W4236765221","https://openalex.org/W6743518132"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W4384486036","https://openalex.org/W1503094549","https://openalex.org/W4229448053","https://openalex.org/W4247925126","https://openalex.org/W2337920774","https://openalex.org/W4286908577","https://openalex.org/W2886410948","https://openalex.org/W2025875869"],"abstract_inverted_index":{"Fast":[0],"and":[1,6,28,43,111,137,155,179],"efficient":[2,48],"access":[3],"to":[4,11,32,50,60,72,88,107,116,159],"environmental":[5],"life":[7],"data":[8,35,85],"is":[9],"key":[10],"the":[12,74,174],"successful":[13],"disaster":[14,52,70,150],"response.":[15],"Vehicular":[16],"crowdsourcing":[17],"(VC)":[18],"by":[19],"a":[20,62,67,95,102,112,123,148],"group":[21,63],"of":[22,64,76,169,176],"unmanned":[23,29],"vehicles":[24,31],"(UVs)":[25],"like":[26],"drones":[27],"ground":[30],"collect":[33],"these":[34],"from":[36],"Point-of-Interests":[37],"(PoIs)":[38],"e.g.,":[39],"possible":[40],"survivor":[41],"spots":[42],"fire":[44],"site,":[45],"provides":[46],"an":[47],"way":[49],"assist":[51],"rescue.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57],"explicitly":[58],"consider":[59],"navigate":[61],"UVs":[65],"in":[66,167],"3-dimensional":[68],"(3D)":[69],"workzone":[71],"maximize":[73],"amount":[75],"collected":[77],"data,":[78],"geographical":[79],"fairness,":[80],"energy":[81,170],"efficiency,":[82,110],"while":[83],"minimizing":[84],"dropout":[86],"due":[87],"limited":[89],"transmission":[90],"rate.":[91],"We":[92,120,146],"propose":[93],"DRL-DisasterVC(3D),":[94],"distributed":[96],"deep":[97],"reinforcement":[98],"learning":[99,109,118],"framework,":[100],"with":[101,130],"repetitive":[103],"experience":[104],"replay":[105],"(RER)":[106],"improve":[108],"clipped":[113],"target":[114],"network":[115,127],"increase":[117],"stability.":[119],"also":[121],"use":[122],"3D":[124],"convolutional":[125],"neural":[126],"(3D":[128],"CNN)":[129],"multi-head-relational":[131],"attention":[132],"(MHRA)":[133],"for":[134,143],"spatial":[135,144],"modeling,":[136],"add":[138],"auxiliary":[139],"pixel":[140],"control":[141],"(PC)":[142],"exploration.":[145],"designed":[147],"novel":[149],"response":[151],"simulator,":[152],"called":[153],"\"DisasterSim\",":[154],"conduct":[156],"extensive":[157],"experiments":[158],"show":[160],"that":[161],"DRL-DisasterVC(3D)":[162],"outperforms":[163],"all":[164],"five":[165],"baselines":[166],"terms":[168],"efficiency":[171],"when":[172],"varying":[173],"numbers":[175],"UVs,":[177],"PoIs":[178],"SNR":[180],"threshold.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-01-14T23:40:02.550235","created_date":"2025-10-10T00:00:00"}