{"id":"https://openalex.org/W7138866335","doi":"https://doi.org/10.1109/globecom59602.2025.11432330","title":"Compressing Data and Deep Learning Models for Green Edge Computing","display_name":"Compressing Data and Deep Learning Models for Green Edge Computing","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7138866335","doi":"https://doi.org/10.1109/globecom59602.2025.11432330"},"language":null,"primary_location":{"id":"doi:10.1109/globecom59602.2025.11432330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5019325599","display_name":"John Violos","orcid":"https://orcid.org/0000-0003-4219-3915"},"institutions":[{"id":"https://openalex.org/I9736820","display_name":"\u00c9cole de Technologie Sup\u00e9rieure","ror":"https://ror.org/0020snb74","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I9736820"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"John Violos","raw_affiliation_strings":["&#x00C9;cole de Technologie Sup&#x00E9;rieure,Department of Software and IT Engineering,Montreal,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"&#x00C9;cole de Technologie Sup&#x00E9;rieure,Department of Software and IT Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I9736820"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130035351","display_name":"Ioannis Fovakis","orcid":null},"institutions":[{"id":"https://openalex.org/I32762134","display_name":"Harokopio University of Athens","ror":"https://ror.org/02k5gp281","country_code":"GR","type":"education","lineage":["https://openalex.org/I32762134"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ioannis Fovakis","raw_affiliation_strings":["Harokopio University,Dept. of Informatics &#x0026; Telematics,Athens,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harokopio University,Dept. of Informatics &#x0026; Telematics,Athens,Greece","institution_ids":["https://openalex.org/I32762134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003789929","display_name":"Aris Leivadeas","orcid":"https://orcid.org/0000-0002-2996-6824"},"institutions":[{"id":"https://openalex.org/I9736820","display_name":"\u00c9cole de Technologie Sup\u00e9rieure","ror":"https://ror.org/0020snb74","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I9736820"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Aris Leivadeas","raw_affiliation_strings":["&#x00C9;cole de Technologie Sup&#x00E9;rieure,Department of Software and IT Engineering,Montreal,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"&#x00C9;cole de Technologie Sup&#x00E9;rieure,Department of Software and IT Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I9736820"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4693","last_page":"4698"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.3529999852180481,"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":0.3529999852180481,"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/T14347","display_name":"Big Data and Digital Economy","score":0.21490000188350677,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12238","display_name":"Green IT and Sustainability","score":0.09160000085830688,"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/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.685699999332428},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.6553000211715698},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.6510999798774719},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6173999905586243},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5529999732971191},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5426999926567078},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4871000051498413},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.42500001192092896},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.4163999855518341},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.4092999994754791}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7642999887466431},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.685699999332428},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.6553000211715698},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.6510999798774719},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6173999905586243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5903000235557556},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5529999732971191},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5426999926567078},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4871000051498413},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.42500001192092896},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.4163999855518341},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.36880001425743103},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35269999504089355},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C153914771","wikidata":"https://www.wikidata.org/wiki/Q5227343","display_name":"Data reduction","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.2637999951839447},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2590999901294708},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/globecom59602.2025.11432330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:espace2.etsmtl.ca:33713","is_oa":false,"landing_page_url":"https://espace2.etsmtl.ca/id/eprint/33713/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402392","display_name":"Espace \u00c9TS (ETS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1341030882","host_organization_name":"Educational Testing Service","host_organization_lineage":["https://openalex.org/I1341030882"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Compte rendu de conf\u00e9rence"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.6662454605102539,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2031614119","https://openalex.org/W2194775991","https://openalex.org/W2789561488","https://openalex.org/W3013559546","https://openalex.org/W3034368386","https://openalex.org/W3130391646","https://openalex.org/W3149749826","https://openalex.org/W3167976421","https://openalex.org/W3172744943","https://openalex.org/W4211124824","https://openalex.org/W4220876727","https://openalex.org/W4280617669","https://openalex.org/W4390238639","https://openalex.org/W4404577050","https://openalex.org/W4405909160"],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"expansion":[2],"of":[3,33,78],"Artificial":[4],"Intelligence":[5],"(AI)":[6],"applications":[7],"at":[8],"the":[9,31,76],"Edge":[10,20,156],"has":[11],"created":[12],"an":[13],"increasing":[14],"demand":[15],"for":[16],"energy-efficient":[17],"deployment.":[18],"Furthermore,":[19],"devices":[21],"are":[22],"inherently":[23],"constrained":[24],"in":[25,111,122,127],"computation,":[26],"bandwidth,":[27,165],"and":[28,37,49,94,131,166],"storage,":[29],"making":[30],"exploration":[32],"compressed":[34,72,79,102],"AI":[35,80,128,157],"models":[36],"data":[38,73,135,151],"a":[39,112,119],"worthwhile":[40],"approach":[41,117],"to":[42,55,74,100,133,145],"enhancing":[43],"efficiency.":[44],"While":[45],"compression":[46,126],"improves":[47],"resource":[48],"energy":[50,123],"efficiency,":[51],"it":[52],"often":[53],"leads":[54],"significant":[56],"performance":[57,106,139,161],"degradation,":[58],"especially":[59],"with":[60,138,159],"complex":[61],"data.":[62],"To":[63],"address":[64],"this":[65],"challenge,":[66],"we":[67],"propose":[68],"leveraging":[69],"transformations":[70,86,99,152],"on":[71,109],"enhance":[75],"effectiveness":[77],"models.":[81],"We":[82],"evaluate":[83],"13":[84],"different":[85],"across":[87],"three":[88],"benchmark":[89],"datasets":[90],"(MNIST,":[91],"FashionMNIST,":[92],"CIFAR-10)":[93],"find":[95],"that":[96,150],"applying":[97],"shadow":[98],"lossy":[101],"images":[103],"significantly":[104],"mitigates":[105],"loss.":[107],"Based":[108],"experiments":[110],"real":[113],"edge":[114],"device":[115],"our":[116],"achieves":[118],"10.7%":[120],"reduction":[121],"consumption,":[124],"99.48%":[125],"model":[129],"architecture,":[130],"up":[132],"72%":[134],"size":[136],"reduction,":[137],"degradation":[140],"ranging":[141],"only":[142],"from":[143],"0.14%":[144],"0.89%.":[146],"These":[147],"results":[148],"show":[149],"can":[153],"enable":[154],"efficient":[155],"inference":[158],"minimal":[160],"loss,":[162],"reducing":[163],"energy,":[164],"computation.":[167]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-20T00:00:00"}
