{"id":"https://openalex.org/W3210969948","doi":"https://doi.org/10.1109/tnse.2022.3180632","title":"Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT","display_name":"Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT","publication_year":2022,"publication_date":"2022-06-08","ids":{"openalex":"https://openalex.org/W3210969948","doi":"https://doi.org/10.1109/tnse.2022.3180632","mag":"3210969948"},"language":"en","primary_location":{"id":"doi:10.1109/tnse.2022.3180632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2022.3180632","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/98076/2/Lisheng_TNSE_22.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054443310","display_name":"Shunpu Tang","orcid":"https://orcid.org/0000-0002-3126-7731"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunpu Tang","raw_affiliation_strings":["School of Computer Science, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3126-7731","affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058300564","display_name":"Lunyuan Chen","orcid":"https://orcid.org/0009-0005-2846-8064"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lunyuan Chen","raw_affiliation_strings":["School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049146326","display_name":"Ke He","orcid":"https://orcid.org/0000-0002-6618-5568"},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]},{"id":"https://openalex.org/I4405259781","display_name":"Interdisciplinary Centre for Security, Reliability and Trust","ror":"https://ror.org/02qav7c04","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577","https://openalex.org/I4405259781"]}],"countries":["CN","LU"],"is_corresponding":false,"raw_author_name":"Ke He","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","Signal Processing and Satellite Communications Research Group (SIGCOM), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg"],"raw_orcid":"https://orcid.org/0000-0002-6618-5568","affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"Signal Processing and Satellite Communications Research Group (SIGCOM), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg","institution_ids":["https://openalex.org/I186903577","https://openalex.org/I4405259781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051086639","display_name":"Junjuan Xia","orcid":"https://orcid.org/0000-0003-2787-6582"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjuan Xia","raw_affiliation_strings":["School of Computer Science, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2787-6582","affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037957923","display_name":"Lisheng Fan","orcid":"https://orcid.org/0000-0002-9783-1366"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lisheng Fan","raw_affiliation_strings":["School of Computer Science, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9783-1366","affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002265731","display_name":"Arumugam Nallanathan","orcid":"https://orcid.org/0000-0001-8337-5884"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Arumugam Nallanathan","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0001-8337-5884","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.1345,"has_fulltext":true,"cited_by_count":139,"citation_normalized_percentile":{"value":0.99306568,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"10","issue":"5","first_page":"2881","last_page":"2893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9997000098228455,"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":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T12079","display_name":"IoT Networks and Protocols","score":0.9970999956130981,"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":0.8363264799118042},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.6626905202865601},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.6236011981964111},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.6091489195823669},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5296912789344788},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.5264747142791748},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.516788125038147},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5024333000183105},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.46235427260398865},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4407447576522827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4359571039676666},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4219379127025604},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.4103900194168091},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3063162565231323},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.21527373790740967},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12028753757476807}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8363264799118042},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.6626905202865601},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.6236011981964111},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.6091489195823669},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5296912789344788},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.5264747142791748},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.516788125038147},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5024333000183105},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.46235427260398865},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4407447576522827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4359571039676666},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4219379127025604},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.4103900194168091},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3063162565231323},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.21527373790740967},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12028753757476807},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnse.2022.3180632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2022.3180632","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","raw_type":"journal-article"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/98076","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/98076","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/98076/2/Lisheng_TNSE_22.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/98076","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/98076","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/98076/2/Lisheng_TNSE_22.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},"sustainable_development_goals":[{"score":0.6600000262260437,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2101739700","display_name":null,"funder_award_id":"YJ2021003","funder_id":"https://openalex.org/F4320328715","funder_display_name":"Guangzhou University"},{"id":"https://openalex.org/G7258998675","display_name":null,"funder_award_id":"61871139/62101145","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8521453224","display_name":null,"funder_award_id":"2021A1515011392","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null},{"id":"https://openalex.org/F4320328715","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3210969948.pdf"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2105103777","https://openalex.org/W2194775991","https://openalex.org/W2195423816","https://openalex.org/W2279098554","https://openalex.org/W2416799949","https://openalex.org/W2541884796","https://openalex.org/W2605258629","https://openalex.org/W2798720628","https://openalex.org/W2896180420","https://openalex.org/W2963451732","https://openalex.org/W2964061570","https://openalex.org/W2964299589","https://openalex.org/W2971544482","https://openalex.org/W2981845057","https://openalex.org/W2981884310","https://openalex.org/W2982083293","https://openalex.org/W2994080478","https://openalex.org/W3034590782","https://openalex.org/W3043715968","https://openalex.org/W3092635848","https://openalex.org/W3093755649","https://openalex.org/W3105122387","https://openalex.org/W3105886168","https://openalex.org/W3108383389","https://openalex.org/W3110777925","https://openalex.org/W3120353641","https://openalex.org/W3127743396","https://openalex.org/W3133354246","https://openalex.org/W3153791152","https://openalex.org/W3160892955","https://openalex.org/W3162126687","https://openalex.org/W3168953114","https://openalex.org/W3170689564","https://openalex.org/W3176707157","https://openalex.org/W3207233526","https://openalex.org/W3214304699","https://openalex.org/W4205940831","https://openalex.org/W4206632990","https://openalex.org/W4225991562","https://openalex.org/W6638523607","https://openalex.org/W6728757088","https://openalex.org/W6779060689"],"related_works":["https://openalex.org/W3116709161","https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W3166492421","https://openalex.org/W4313463218","https://openalex.org/W4312996489","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W3106131444","https://openalex.org/W3216099748"],"abstract_inverted_index":{"In":[0,19,108,200],"this":[1,20,73,109],"paper,":[2],"we":[3,75,150],"investigate":[4],"how":[5],"to":[6,37,58,65,137,161,211],"deploy":[7],"computational":[8,46,115,132],"intelligence":[9],"and":[10,49,62,98,122,134,157,184,189],"deep":[11,87],"learning":[12,82],"(DL)":[13],"in":[14,40,69,104,168,213],"edge-enabled":[15],"industrial":[16,42,215],"IoT":[17,23,43,216],"networks.":[18],"system,":[21],"the":[22,41,66,86,102,105,111,119,125,163,177,193,197,202,214,219],"devices":[24,57,112,136],"can":[25,89,117,129,205],"collaboratively":[26],"train":[27],"a":[28,77,146,152,182],"shared":[29],"model":[30,88],"without":[31],"compromising":[32],"data":[33],"privacy.":[34],"However,":[35],"due":[36],"limited":[38,221],"resources":[39],"networks,":[44],"including":[45],"power,":[47],"bandwidth,":[48],"channel":[50],"state,":[51],"it":[52,190],"is":[53],"challenging":[54],"for":[55],"many":[56],"accomplish":[59],"local":[60],"training":[61,124],"upload":[63],"weights":[64],"edge":[67,81],"server":[68],"time.":[70],"To":[71],"address":[72],"issue,":[74],"propose":[76,151],"novel":[78],"multi-exit-based":[79],"federated":[80],"(ME-FEEL)":[83],"framework,":[84],"where":[85],"be":[90],"divided":[91],"into":[92,139],"several":[93],"sub-models":[94],"with":[95,113,218],"different":[96],"depths":[97],"output":[99],"prediction":[100],"from":[101],"exit":[103,155],"corresponding":[106],"sub-model.":[107],"way,":[110],"insufficient":[114],"power":[116],"choose":[118],"earlier":[120],"exits":[121,167],"avoid":[123],"complete":[126],"model,":[127],"which":[128],"help":[130],"reduce":[131],"latency":[133,147],"enable":[135],"participate":[138],"aggregation":[140],"as":[141,143],"much":[142],"possible":[144],"within":[145],"threshold.":[148],"Moreover,":[149],"greedy":[153],"approach-based":[154],"selection":[156],"bandwidth":[158],"allocation":[159],"algorithm":[160],"maximize":[162],"total":[164],"number":[165],"of":[166],"each":[169],"communication":[170],"round.":[171],"Simulation":[172],"experiments":[173],"are":[174],"conducted":[175],"on":[176],"classical":[178],"Fashion-MNIST":[179],"dataset":[180],"under":[181],"non-independent":[183],"identically":[185],"distributed":[186],"(non-IID)":[187],"setting,":[188],"shows":[191],"that":[192],"proposed":[194,203],"strategy":[195],"outperforms":[196],"conventional":[198],"FL.":[199],"particular,":[201],"ME-FEEL":[204],"achieve":[206],"an":[207],"accuracy":[208],"gain":[209],"up":[210],"32.7%":[212],"networks":[217],"severely":[220],"resources.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":16}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}