{"id":"https://openalex.org/W4416062053","doi":"https://doi.org/10.1109/percom67906.2026.11524531","title":"ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge","display_name":"ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W4416062053","doi":"https://doi.org/10.1109/percom67906.2026.11524531"},"language":"en","primary_location":{"id":"doi:10.1109/percom67906.2026.11524531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom67906.2026.11524531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.06011","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098960058","display_name":"Daghash K. Alqahtani","orcid":"https://orcid.org/0009-0001-5309-6996"},"institutions":[{"id":"https://openalex.org/I4210139057","display_name":"AARNet (Australia)","ror":"https://ror.org/03j2gem75","country_code":"AU","type":"company","lineage":["https://openalex.org/I4210139057"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daghash K. Alqahtani","raw_affiliation_strings":["The University of Melbourne,Distributed Systems and Network Applications (DisNet) Laboratory,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,Distributed Systems and Network Applications (DisNet) Laboratory,Australia","institution_ids":["https://openalex.org/I4210139057"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032796449","display_name":"Maria A. Rodriguez","orcid":"https://orcid.org/0000-0002-2831-8526"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Maria A. Rodriguez","raw_affiliation_strings":["The University of Melbourne,School of Computing and Information Systems,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,School of Computing and Information Systems,Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047549903","display_name":"Muhammad Aamir Cheema","orcid":"https://orcid.org/0000-0003-2139-9121"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Muhammad Aamir Cheema","raw_affiliation_strings":["Monash University,Department of Software Systems and Cybersecurity,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University,Department of Software Systems and Cybersecurity,Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099282607","display_name":"Hamid Rezatofighi","orcid":null},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hamid Rezatofighi","raw_affiliation_strings":["Monash University,Department of Data Science and AI,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University,Department of Data Science and AI,Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083902835","display_name":"Adel N. Toosi","orcid":"https://orcid.org/0000-0001-5655-5337"},"institutions":[{"id":"https://openalex.org/I4210139057","display_name":"AARNet (Australia)","ror":"https://ror.org/03j2gem75","country_code":"AU","type":"company","lineage":["https://openalex.org/I4210139057"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Adel N. Toosi","raw_affiliation_strings":["The University of Melbourne,Distributed Systems and Network Applications (DisNet) Laboratory,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,Distributed Systems and Network Applications (DisNet) Laboratory,Australia","institution_ids":["https://openalex.org/I4210139057"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8388000130653381,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.8388000130653381,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.07620000094175339,"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":0.013500000350177288,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6902999877929688},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.593999981880188},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5745000243186951},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.5372999906539917},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5339000225067139},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5223000049591064},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43790000677108765},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.42559999227523804},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41929998993873596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7412999868392944},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6902999877929688},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.593999981880188},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5745000243186951},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.5372999906539917},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5339000225067139},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4596000015735626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4198000133037567},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.41589999198913574},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.375},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3402000069618225},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.32839998602867126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3246999979019165},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.26010000705718994}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/percom67906.2026.11524531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom67906.2026.11524531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.06011","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.06011","pdf_url":"https://arxiv.org/pdf/2507.06011","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:doi:10.48550/arxiv.2507.06011","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2507.06011","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.06011","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.06011","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.06011","pdf_url":"https://arxiv.org/pdf/2507.06011","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Edge":[0],"computing":[1],"enables":[2],"data":[3],"processing":[4,80],"closer":[5],"to":[6,77,82,172],"the":[7,40,83],"source,":[8],"significantly":[9],"reducing":[10],"latency,":[11],"an":[12,72],"essential":[13],"requirement":[14],"for":[15],"real-time":[16],"vision-based":[17],"analytics":[18],"such":[19],"as":[20],"object":[21,99,121],"detection":[22,47,95,122,169],"in":[23,168],"surveillance":[24],"and":[25,46,71,94,127,138,140,155,159],"smart":[26],"city":[27],"environments.":[28],"However,":[29],"these":[30],"tasks":[31],"place":[32],"substantial":[33],"demands":[34],"on":[35,98,108],"resource-constrained":[36],"edge":[37,86,129],"devices,":[38],"making":[39],"joint":[41],"optimization":[42],"of":[43],"energy":[44,92,153],"consumption":[45,154],"accuracy":[48,170],"critical.":[49],"To":[50],"address":[51],"this":[52],"challenge,":[53],"we":[54],"propose":[55],"ECORE":[56,89],",":[57],"a":[58,67,165],"framework":[59,104],"that":[60,145],"integrates":[61],"multiple":[62],"dynamic":[63],"routing":[64,149],"strategies,":[65],"including":[66,131],"novel":[68],"estimation-based":[69],"techniques":[70],"innovative":[73],"greedy":[74],"selection":[75],"algorithm,":[76],"direct":[78],"image":[79],"requests":[81],"most":[84],"suitable":[85],"device\u2013model":[87],"pair.":[88],"dynamically":[90],"balances":[91],"efficiency":[93],"performance":[96],"based":[97],"characteristics.":[100],"We":[101],"evaluate":[102],"our":[103,146],"through":[105],"extensive":[106],"experiments":[107],"real-world":[109],"datasets,":[110],"comparing":[111],"against":[112],"widely":[113],"used":[114],"baseline":[115],"techniques.":[116],"The":[117],"evaluation":[118],"leverages":[119],"established":[120],"models":[123],"(YOLO,":[124],"SSD,":[125],"EfficientDet)":[126],"diverse":[128],"platforms,":[130],"Jetson":[132],"Orin":[133],"Nano,":[134],"Raspberry":[135],"Pi":[136],"4":[137],"5,":[139],"TPU":[141],"accelerators.":[142],"Results":[143],"demonstrate":[144],"proposed":[147],"context-aware":[148],"strategies":[150],"can":[151],"reduce":[152],"latency":[156],"by":[157],"35%":[158],"49%,":[160],"respectively,":[161],"while":[162],"incurring":[163],"only":[164],"2%":[166],"loss":[167],"compared":[171],"accuracy-centric":[173],"methods.":[174]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}