{"id":"https://openalex.org/W4406059198","doi":"https://doi.org/10.48550/arxiv.2407.13360","title":"Ultra-Low-Latency Edge Inference for Distributed Sensing","display_name":"Ultra-Low-Latency Edge Inference for Distributed Sensing","publication_year":2024,"publication_date":"2024-07-18","ids":{"openalex":"https://openalex.org/W4406059198","doi":"https://doi.org/10.48550/arxiv.2407.13360"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2407.13360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.13360","pdf_url":"https://arxiv.org/pdf/2407.13360","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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2407.13360","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101409606","display_name":"Zhanwei Wang","orcid":"https://orcid.org/0009-0000-6403-1429"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Zhanwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073062583","display_name":"Anders E. Kal\u00f8r","orcid":"https://orcid.org/0000-0003-4096-2389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kal\u00f8r, Anders E.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008655239","display_name":"You Zhou","orcid":"https://orcid.org/0000-0002-5810-2347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, You","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071289803","display_name":"Petar Popovski","orcid":"https://orcid.org/0000-0001-6195-4797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Popovski, Petar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007131492","display_name":"Kaibin Huang","orcid":"https://orcid.org/0000-0001-8773-4629"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Kaibin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101409606"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9420999884605408,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9420999884605408,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9007999897003174,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/inference","display_name":"Inference","score":0.660392701625824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5998724699020386},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5589143633842468},{"id":"https://openalex.org/keywords/low-latency","display_name":"Low latency (capital markets)","score":0.5520270466804504},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5231472253799438},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2615480124950409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22335609793663025},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2163793444633484}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.660392701625824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5998724699020386},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5589143633842468},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.5520270466804504},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5231472253799438},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2615480124950409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22335609793663025},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2163793444633484}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2407.13360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.13360","pdf_url":"https://arxiv.org/pdf/2407.13360","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:oai:pure.atira.dk:openaire/c06fca00-2bfa-48fe-9dc4-8c70457eaa2d","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/c06fca00-2bfa-48fe-9dc4-8c70457eaa2d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Wang, Z, Kal\u00f8r, A E, Zhou, Y, Popovski, P & Huang, K 2025 'Ultra-Low-Latency Edge Inference for Distributed Sensing' arXiv. https://doi.org/10.48550/arXiv.2407.13360","raw_type":"workingPaper"},{"id":"doi:10.48550/arxiv.2407.13360","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2407.13360","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2407.13360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.13360","pdf_url":"https://arxiv.org/pdf/2407.13360","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/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4406059198.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3128807919","https://openalex.org/W3176411177","https://openalex.org/W3205411230","https://openalex.org/W4286899009","https://openalex.org/W9168048","https://openalex.org/W4300849822","https://openalex.org/W4376480820","https://openalex.org/W3155891479","https://openalex.org/W3029351463","https://openalex.org/W4308600690"],"abstract_inverted_index":{"There":[0],"is":[1],"a":[2,11,20,183],"broad":[3],"consensus":[4],"that":[5,109,148,167],"artificial":[6],"intelligence":[7],"(AI)":[8],"will":[9,25],"be":[10],"defining":[12],"component":[13],"of":[14,92,113,139,158],"the":[15,33,66,81,88,111,114,131,137,151,156,159,168],"sixth-generation":[16],"(6G)":[17],"networks.":[18],"As":[19],"specific":[21],"instance,":[22],"AI-empowered":[23],"sensing":[24,40,116,120,140,180],"gather":[26],"and":[27,41,51,56,69,78,126,136,165],"process":[28],"environmental":[29],"perception":[30],"data":[31,82],"at":[32],"network":[34],"edge,":[35],"giving":[36],"rise":[37],"to":[38,80,179],"integrated":[39],"edge":[42],"AI":[43],"(ISEA).":[44],"Many":[45],"applications,":[46],"such":[47],"as":[48],"autonomous":[49],"driving":[50],"industrial":[52],"manufacturing,":[53],"are":[54],"latency-sensitive":[55],"require":[57],"end-to-end":[58],"(E2E)":[59],"performance":[60,91,181],"guarantees":[61],"under":[62,182],"stringent":[63],"deadlines.":[64],"However,":[65],"5G-style":[67],"ultra-reliable":[68],"low-latency":[70],"communication":[71,76,124],"(URLLC)":[72],"techniques":[73],"designed":[74],"with":[75,177],"reliability":[77,125],"agnostic":[79],"may":[83],"fall":[84],"short":[85],"in":[86,118],"achieving":[87],"optimal":[89,152],"E2E":[90,115],"perceptive":[93,107],"wireless":[94],"systems.":[95],"In":[96],"this":[97],"work,":[98],"we":[99,142],"introduce":[100],"an":[101,144],"ultra-low-latency":[102],"(ultra-LoLa)":[103],"inference":[104,127,171],"framework":[105,172],"for":[106],"networks":[108],"facilitates":[110],"analysis":[112],"accuracy":[117,157],"distributed":[119],"by":[121],"jointly":[122],"considering":[123],"accuracy.":[128],"By":[129],"characterizing":[130],"tradeoff":[132],"between":[133],"packet":[134],"length":[135],"number":[138],"observations,":[141],"derive":[143],"efficient":[145],"optimization":[146],"procedure":[147],"closely":[149],"approximates":[150],"tradeoff.":[153],"We":[154],"validate":[155],"proposed":[160,169],"method":[161],"through":[162],"experimental":[163],"results,":[164],"show":[166],"ultra-Lola":[170],"outperforms":[173],"conventional":[174],"reliability-oriented":[175],"protocols":[176],"respect":[178],"latency":[184],"constraint.":[185]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
