{"id":"https://openalex.org/W4392666388","doi":"https://doi.org/10.1109/tce.2024.3375859","title":"Human Activity Recognition in Mobile Edge Computing: A Low-Cost and High-Fidelity Digital Twin Approach With Deep Reinforcement Learning","display_name":"Human Activity Recognition in Mobile Edge Computing: A Low-Cost and High-Fidelity Digital Twin Approach With Deep Reinforcement Learning","publication_year":2024,"publication_date":"2024-03-11","ids":{"openalex":"https://openalex.org/W4392666388","doi":"https://doi.org/10.1109/tce.2024.3375859"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2024.3375859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3375859","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-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/A5100385887","display_name":"Chenyu Wang","orcid":"https://orcid.org/0000-0003-3187-9444"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chenyu Wang","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0003-3187-9444","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072627238","display_name":"Zhipeng Cai","orcid":"https://orcid.org/0000-0001-6017-975X"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhipeng Cai","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0001-6017-975X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046635673","display_name":"Yingshu Li","orcid":"https://orcid.org/0000-0002-1906-7112"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingshu Li","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0002-1906-7112","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100385887"],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":5.6621,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.95921291,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"71","issue":"2","first_page":"6451","last_page":"6459"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.9484999775886536,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10763","display_name":"Digital Transformation in Industry","score":0.9484999775886536,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.6912837028503418},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6850886344909668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4847702383995056},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.4842779040336609},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4842219352722168},{"id":"https://openalex.org/keywords/mobile-edge-computing","display_name":"Mobile edge computing","score":0.4719521105289459},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.40808579325675964},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.40293964743614197},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3697808086872101},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.33592116832733154},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2077639102935791},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1718955636024475},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.12979531288146973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912837028503418},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6850886344909668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4847702383995056},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.4842779040336609},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4842219352722168},{"id":"https://openalex.org/C2776061582","wikidata":"https://www.wikidata.org/wiki/Q25325231","display_name":"Mobile edge computing","level":3,"score":0.4719521105289459},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.40808579325675964},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.40293964743614197},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3697808086872101},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.33592116832733154},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2077639102935791},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1718955636024475},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.12979531288146973}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2024.3375859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3375859","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4354139244","display_name":null,"funder_award_id":"2244219","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G551916812","display_name":null,"funder_award_id":"2315596","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6193564845","display_name":null,"funder_award_id":"2146497","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W169931978","https://openalex.org/W1794039122","https://openalex.org/W1987522330","https://openalex.org/W2059732136","https://openalex.org/W2279251824","https://openalex.org/W2572361735","https://openalex.org/W2597150627","https://openalex.org/W2897807975","https://openalex.org/W2904287723","https://openalex.org/W2909260516","https://openalex.org/W2919138664","https://openalex.org/W2940791683","https://openalex.org/W2944851425","https://openalex.org/W2966450377","https://openalex.org/W2967569340","https://openalex.org/W2971209636","https://openalex.org/W3017361427","https://openalex.org/W3022522270","https://openalex.org/W3033183874","https://openalex.org/W3045059767","https://openalex.org/W3090437903","https://openalex.org/W3100789280","https://openalex.org/W3128348732","https://openalex.org/W3164089829","https://openalex.org/W3175101164","https://openalex.org/W3205137522","https://openalex.org/W4210698675","https://openalex.org/W4213443727","https://openalex.org/W4225537087","https://openalex.org/W4225899372","https://openalex.org/W4285237823","https://openalex.org/W4285295176","https://openalex.org/W4362563552","https://openalex.org/W4362591411","https://openalex.org/W4366250585","https://openalex.org/W4386041553","https://openalex.org/W4386041566","https://openalex.org/W4386261508","https://openalex.org/W4386262998","https://openalex.org/W4386418529","https://openalex.org/W4387303325","https://openalex.org/W4388756760"],"related_works":["https://openalex.org/W4313443006","https://openalex.org/W2945374968","https://openalex.org/W4385452045","https://openalex.org/W4293777179","https://openalex.org/W2164070813","https://openalex.org/W2135608140","https://openalex.org/W2895525995","https://openalex.org/W2332512904","https://openalex.org/W2319626700","https://openalex.org/W4319589573"],"abstract_inverted_index":{"The":[0,47,83],"presence":[1],"of":[2,18,49,99,159],"digital":[3],"twins":[4],"(DTs)":[5],"has":[6,54],"expanded":[7],"within":[8],"the":[9,33,79,92,112,136,156],"consumer":[10,38,165],"electronics":[11,39],"area":[12],"due":[13,41],"to":[14,42,59,115],"its":[15],"inherent":[16],"benefits":[17],"high-fidelity":[19],"modeling":[20,62],"and":[21,29,45,63,123,135,164],"predictive":[22],"insight.":[23],"However,":[24],"DT":[25,56,75,160],"deployment":[26,57,76],"remains":[27],"costly":[28],"constrained":[30],"in":[31,78,150,162],"providing":[32],"fundamental":[34],"functionalities":[35],"required":[36],"by":[37],"systems":[40],"massive":[43],"computation":[44],"communication.":[46],"emergence":[48],"mobile":[50],"edge":[51],"computing":[52],"(MEC)":[53],"made":[55],"feasible":[58],"achieve":[60],"data-driven":[61],"consumer-centric":[64],"concurrently":[65],"with":[66,104,130],"low":[67],"communication":[68],"costs.":[69],"This":[70],"article":[71],"proposes":[72],"an":[73],"MEC-based":[74],"scheme":[77,141],"smart":[80],"home":[81],"domain.":[82],"cloud":[84],"service":[85],"can":[86,110],"predict":[87],"subsequent":[88],"sensor":[89,117,144],"updates":[90],"leveraging":[91],"MEC":[93,163],"platform\u2019s":[94],"human":[95],"activity":[96],"recognition":[97],"result":[98],"residential":[100,133,152],"environments.":[101],"In":[102],"addition,":[103],"deep":[105],"reinforcement":[106],"learning":[107],"(DRL),":[108],"it":[109],"track":[111],"essential":[113],"data":[114],"maintain":[116],"update":[118,145],"consistency":[119,146],"between":[120],"both":[121],"physical":[122],"virtual":[124],"sides.":[125],"We":[126],"implement":[127],"experimental":[128],"evaluation":[129],"two":[131],"real-world":[132],"datasets,":[134],"results":[137],"demonstrate":[138],"that":[139],"our":[140],"maintains":[142],"high-level":[143],"while":[147],"being":[148],"energy-efficient":[149],"different":[151],"environments,":[153],"which":[154],"illuminates":[155],"promising":[157],"prospects":[158],"implementation":[161],"electronics.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
