{"id":"https://openalex.org/W4400727455","doi":"https://doi.org/10.1109/iwcmc61514.2024.10592446","title":"Cost-Efficient Computation Offloading in VEC Using Deep Reinforcement Learning Techniques","display_name":"Cost-Efficient Computation Offloading in VEC Using Deep Reinforcement Learning Techniques","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4400727455","doi":"https://doi.org/10.1109/iwcmc61514.2024.10592446"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc61514.2024.10592446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc61514.2024.10592446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Wireless Communications and Mobile Computing (IWCMC)","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/A5101685808","display_name":"Bingxin Wang","orcid":"https://orcid.org/0000-0002-8020-7014"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bingxin Wang","raw_affiliation_strings":["Beijing Research Institute, China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Institute, China,Beijing,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111259855","display_name":"Dan Tu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Tu","raw_affiliation_strings":["Beijing Research Institute, China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Institute, China,Beijing,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602001","display_name":"Jie Wang","orcid":"https://orcid.org/0000-0001-5442-9505"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Wang","raw_affiliation_strings":["Beijing Research Institute, China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Institute, China,Beijing,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101685808"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0958,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78158604,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"296","last_page":"300"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9524000287055969,"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"}},"topics":[{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9524000287055969,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8402718901634216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290622591972351},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6973437070846558},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5410578846931458},{"id":"https://openalex.org/keywords/computation-offloading","display_name":"Computation offloading","score":0.512246310710907},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.360637903213501},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13061794638633728},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.10779374837875366}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8402718901634216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290622591972351},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6973437070846558},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5410578846931458},{"id":"https://openalex.org/C2781041963","wikidata":"https://www.wikidata.org/wiki/Q18348618","display_name":"Computation offloading","level":4,"score":0.512246310710907},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.360637903213501},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13061794638633728},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.10779374837875366},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc61514.2024.10592446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc61514.2024.10592446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Wireless Communications and Mobile Computing (IWCMC)","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":10,"referenced_works":["https://openalex.org/W2955639029","https://openalex.org/W3116213122","https://openalex.org/W3152503594","https://openalex.org/W3162156162","https://openalex.org/W3172105014","https://openalex.org/W3206780742","https://openalex.org/W4285343863","https://openalex.org/W4297911784","https://openalex.org/W4309263401","https://openalex.org/W4387089149"],"related_works":["https://openalex.org/W2751308120","https://openalex.org/W2954039338","https://openalex.org/W4306904969","https://openalex.org/W2966556967","https://openalex.org/W2896883851","https://openalex.org/W2138720691","https://openalex.org/W2337102067","https://openalex.org/W3108946947","https://openalex.org/W4302338237","https://openalex.org/W4381747553"],"abstract_inverted_index":{"The":[0],"rapid":[1],"progression":[2],"in":[3,40,85,99,138,185,199,210],"autonomous":[4],"driving":[5,25],"and":[6,23,36,135,159],"vehicle":[7,16,42],"networking":[8],"technologies":[9],"has":[10],"catalyzed":[11],"the":[12,24,33,79,103,115,119,132,152,181,205],"emergence":[13],"of":[14,105,118,183],"advanced":[15],"applications,":[17],"aiming":[18,149],"to":[19,87,129,150],"augment":[20],"traffic":[21],"safety":[22],"experience.":[26],"This":[27,76,140],"advancement,":[28],"however,":[29],"is":[30,102,142],"challenged":[31],"by":[32],"limited":[34],"computational":[35,58,82],"storage":[37],"capacities":[38],"inherent":[39],"on-board":[41],"systems.":[43],"To":[44],"address":[45,131],"this,":[46],"Vehicle":[47],"Edge":[48],"Computing":[49],"(VEC)":[50],"emerges":[51],"as":[52],"a":[53,65,193],"pivotal":[54],"solution,":[55],"enhancing":[56],"vehicular":[57,200,212],"capabilities.":[59],"In":[60],"this":[61,169],"context,":[62],"we":[63],"introduce":[64,123],"novel":[66],"VEC":[67,186],"task":[68,133],"offloading":[69,92,134,147,198],"model":[70,77],"utilizing":[71],"Deep":[72,110],"Reinforcement":[73,106],"Learning":[74,107,111],"(DRL).":[75],"leverages":[78],"otherwise":[80],"idle":[81],"resources":[83],"available":[84],"vehicles":[86],"facilitate":[88],"efficient":[89],"edge":[90],"computing":[91],"within":[93],"heterogeneous":[94],"networks.":[95],"A":[96],"key":[97],"innovation":[98],"our":[100,165],"approach":[101,172],"integration":[104],"(RL)":[108],"with":[109],"(DL),":[112],"significantly":[113],"improving":[114,180],"convergence":[116],"efficiency":[117],"system.":[120],"We":[121],"also":[122,203],"an":[124],"enhanced":[125],"Q-learning":[126,171],"algorithm":[127,141],"tailored":[128],"jointly":[130],"processing":[136],"challenges":[137],"VEC.":[139],"adept":[143],"at":[144],"making":[145],"optimal":[146],"decisions,":[148],"minimize":[151],"overall":[153],"system":[154,176],"cost,":[155],"encompassing":[156],"both":[157],"latency":[158],"energy":[160],"consumption.":[161],"Through":[162],"rigorous":[163],"simulation,":[164],"results":[166],"demonstrate":[167],"that":[168],"improved":[170],"substantially":[173],"reduces":[174],"total":[175],"costs":[177],"while":[178],"concurrently":[179],"quality":[182],"service":[184],"environments.":[187],"Our":[188],"study":[189],"not":[190],"only":[191],"offers":[192],"robust":[194],"framework":[195],"for":[196,207],"computation":[197],"networks":[201],"but":[202],"paves":[204],"way":[206],"future":[208],"research":[209],"AI-driven":[211],"technology":[213],"optimizations.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
