{"id":"https://openalex.org/W2962834214","doi":"https://doi.org/10.1109/tsusc.2019.2929935","title":"ST-DeLTA: A Novel Spatial-Temporal Value Network Aided Deep Learning Based Intelligent Network Traffic Control System","display_name":"ST-DeLTA: A Novel Spatial-Temporal Value Network Aided Deep Learning Based Intelligent Network Traffic Control System","publication_year":2019,"publication_date":"2019-07-19","ids":{"openalex":"https://openalex.org/W2962834214","doi":"https://doi.org/10.1109/tsusc.2019.2929935","mag":"2962834214"},"language":"en","primary_location":{"id":"doi:10.1109/tsusc.2019.2929935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2019.2929935","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"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 Sustainable Computing","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/A5007662359","display_name":"Fengxiao Tang","orcid":"https://orcid.org/0000-0003-2414-4802"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fengxiao Tang","raw_affiliation_strings":["Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan"],"raw_orcid":"https://orcid.org/0000-0003-2414-4802","affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015739734","display_name":"Bomin Mao","orcid":"https://orcid.org/0000-0001-7780-5972"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Bomin Mao","raw_affiliation_strings":["Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan"],"raw_orcid":"https://orcid.org/0000-0001-7780-5972","affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063911030","display_name":"Zubair Md. Fadlullah","orcid":"https://orcid.org/0000-0002-4785-2425"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zubair Md. Fadlullah","raw_affiliation_strings":["Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan"],"raw_orcid":"https://orcid.org/0000-0002-4785-2425","affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiajia Liu","orcid":"https://orcid.org/0000-0002-9920-4956"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajia Liu","raw_affiliation_strings":["School of Cyberspace Security, Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-9920-4956","affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013311265","display_name":"Nei Kato","orcid":"https://orcid.org/0000-0001-8769-302X"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nei Kato","raw_affiliation_strings":["Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8769-302X","affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8434,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.9150771,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"5","issue":"4","first_page":"568","last_page":"580"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994999766349792,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.5447975993156433},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49880313873291016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4983210563659668},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.45472589135169983},{"id":"https://openalex.org/keywords/spatial-learning","display_name":"Spatial learning","score":0.44611552357673645},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34411320090293884},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.10988563299179077},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10929018259048462}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5447975993156433},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49880313873291016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4983210563659668},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.45472589135169983},{"id":"https://openalex.org/C2985665543","wikidata":"https://www.wikidata.org/wiki/Q3560550","display_name":"Spatial learning","level":3,"score":0.44611552357673645},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34411320090293884},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.10988563299179077},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10929018259048462},{"id":"https://openalex.org/C2781161787","wikidata":"https://www.wikidata.org/wiki/Q48360","display_name":"Hippocampus","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsusc.2019.2929935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2019.2929935","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"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 Sustainable Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1522734439","https://openalex.org/W1983364832","https://openalex.org/W2016053056","https://openalex.org/W2128569883","https://openalex.org/W2285924575","https://openalex.org/W2290469384","https://openalex.org/W2546571074","https://openalex.org/W2566425973","https://openalex.org/W2601253877","https://openalex.org/W2613564606","https://openalex.org/W2617931713","https://openalex.org/W2620303912","https://openalex.org/W2746722165","https://openalex.org/W2753941074","https://openalex.org/W2767151733","https://openalex.org/W2783190965","https://openalex.org/W2783684775","https://openalex.org/W2796121587","https://openalex.org/W2803418695","https://openalex.org/W2810871807","https://openalex.org/W2862352592","https://openalex.org/W2891388911","https://openalex.org/W2891404589","https://openalex.org/W2891768968","https://openalex.org/W2899993822","https://openalex.org/W2916926743","https://openalex.org/W2919115771","https://openalex.org/W2963190722","https://openalex.org/W2963805754","https://openalex.org/W2964121744","https://openalex.org/W3100857292","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W2939353110","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756"],"abstract_inverted_index":{"Deep":[0],"learning":[1,23,74,104],"has":[2,24,176],"emerged":[3],"as":[4,111,137,139],"a":[5,97],"popular":[6],"Artificial":[7],"Intelligence":[8],"(AI)":[9],"technique":[10],"to":[11,50,132],"make":[12,146],"conventional":[13],"cyber":[14],"physical":[15],"systems":[16],"become":[17],"intelligent":[18,106],"and":[19,54,62,123,145,154,168,180,194],"sustainable.":[20],"Recently,":[21],"deep":[22,37,73,103],"been":[25],"widely":[26],"used":[27],"in":[28,65,115,151,187],"the":[29,33,40,58,67,71,82,94,120,135,161,169],"network":[30,42,69,76,101,181],"domain.":[31],"With":[32],"aid":[34],"of":[35,85,142,165,189],"powerful":[36],"neural":[38],"networks,":[39],"communication":[41],"can":[43],"carry":[44],"out":[45],"packets":[46,148,196],"forwarding":[47,149],"actions":[48],"intelligently":[49,133],"avoid":[51],"possible":[52],"failure":[53],"congestion.":[55],"However,":[56],"with":[57,184],"high":[59],"computing":[60,162],"cost":[61,163],"process":[63],"limitation":[64],"only":[66],"static":[68],"scenario,":[70],"existing":[72,95],"based":[75,105],"traffic":[77,107,143],"control":[78,108],"algorithms":[79,186],"cannot":[80],"satisfy":[81],"sustainable":[83],"requirement":[84],"next":[86],"generation":[87],"large":[88,152],"scale":[89,153],"dynamic":[90,155],"network.":[91],"To":[92],"conquer":[93],"problems,":[96],"novel":[98],"spatial-temporal":[99],"value":[100,121],"aided":[102],"algorithm":[109],"referred":[110],"ST-DeLTA":[112],"is":[113],"proposed":[114],"this":[116],"paper.":[117],"In":[118],"ST-DeLTA,":[119],"matrix":[122],"spatial":[124,136],"temporal":[125,140],"training":[126,179,190],"model":[127],"(ST":[128],"model)":[129],"are":[130],"employed":[131],"extract":[134],"well":[138],"features":[141],"patterns":[144],"adaptive":[147],"decision":[150],"networks.":[156],"The":[157],"mathematical":[158],"analysis":[159],"gives":[160],"reduction":[164],"our":[166,174],"proposal,":[167],"computer":[170],"simulation":[171],"demonstrates":[172],"that":[173],"proposal":[175],"significantly":[177],"better":[178],"performance":[182],"compared":[183],"traditional":[185],"terms":[188],"accuracy,":[191],"transmission":[192],"throughput,":[193],"average":[195],"loss":[197],"rate.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
