{"id":"https://openalex.org/W7115735109","doi":"https://doi.org/10.1109/ton.2025.3643286","title":"AoI-Based Scheduling of Correlated Sources for Timely Inference","display_name":"AoI-Based Scheduling of Correlated Sources for Timely Inference","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7115735109","doi":"https://doi.org/10.1109/ton.2025.3643286"},"language":null,"primary_location":{"id":"doi:10.1109/ton.2025.3643286","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2025.3643286","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","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":null,"display_name":"Md Kamran Chowdhury Shisher","orcid":"https://orcid.org/0000-0002-3991-1130"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Md Kamran Chowdhury Shisher","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Vishrant Tripathi","orcid":"https://orcid.org/0000-0001-9892-1366"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishrant Tripathi","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mung Chiang","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mung Chiang","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":null,"display_name":"Christopher G. Brinton","orcid":"https://orcid.org/0000-0003-2771-3521"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher G. Brinton","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62827437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"2181","last_page":"2195"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9991999864578247,"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/T13553","display_name":"Age of Information Optimization","score":0.9991999864578247,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":9.999999747378752e-05,"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/scheduling","display_name":"Scheduling (production processes)","score":0.692799985408783},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.670799970626831},{"id":"https://openalex.org/keywords/penalty-method","display_name":"Penalty method","score":0.5924999713897705},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5698000192642212},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4690000116825104},{"id":"https://openalex.org/keywords/online-algorithm","display_name":"Online algorithm","score":0.4327000081539154},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.43130001425743103},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4077000021934509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7017999887466431},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.692799985408783},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.670799970626831},{"id":"https://openalex.org/C6180225","wikidata":"https://www.wikidata.org/wiki/Q3411771","display_name":"Penalty method","level":2,"score":0.5924999713897705},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5698000192642212},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5091999769210815},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.36320000886917114},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.3190999925136566},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28130000829696655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25839999318122864},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C107568181","wikidata":"https://www.wikidata.org/wiki/Q5319000","display_name":"Dynamic priority scheduling","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ton.2025.3643286","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2025.3643286","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7463008761405945,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G637786994","display_name":null,"funder_award_id":"N00014-23-C-1016","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G6776902378","display_name":null,"funder_award_id":"N00014-22-1-2305","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G848880661","display_name":null,"funder_award_id":"CPS-2313109","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1640967807","https://openalex.org/W1677318746","https://openalex.org/W1993918491","https://openalex.org/W2003798390","https://openalex.org/W2056921512","https://openalex.org/W2097931172","https://openalex.org/W2112848343","https://openalex.org/W2168405694","https://openalex.org/W2511028367","https://openalex.org/W2744248483","https://openalex.org/W2791487310","https://openalex.org/W2947364945","https://openalex.org/W2963270598","https://openalex.org/W2964345816","https://openalex.org/W2970029779","https://openalex.org/W2970983057","https://openalex.org/W2975374931","https://openalex.org/W2975836349","https://openalex.org/W3001922646","https://openalex.org/W3011830499","https://openalex.org/W3022217175","https://openalex.org/W3132179075","https://openalex.org/W3137257456","https://openalex.org/W3164366798","https://openalex.org/W3171374485","https://openalex.org/W3173628078","https://openalex.org/W3175748441","https://openalex.org/W3183158142","https://openalex.org/W3215171387","https://openalex.org/W4297882551","https://openalex.org/W4297882599","https://openalex.org/W4297882647","https://openalex.org/W4382318199","https://openalex.org/W4386918712","https://openalex.org/W4387150077","https://openalex.org/W4387397220","https://openalex.org/W4390189149","https://openalex.org/W4395686776","https://openalex.org/W4399687995","https://openalex.org/W4401508594","https://openalex.org/W4401539202","https://openalex.org/W4401693748","https://openalex.org/W4414538497","https://openalex.org/W4415798493","https://openalex.org/W7084051338"],"related_works":[],"abstract_inverted_index":{"We":[0,167],"investigate":[1],"a":[2,14,18,63,86,93,105,134,147],"real-time":[3],"remote":[4],"inference":[5,57,257],"system":[6,216],"where":[7],"multiple":[8,27,128],"correlated":[9,120],"sources":[10,103],"transmit":[11],"observations":[12,24,37],"over":[13],"communication":[15,33],"channel":[16],"to":[17,25,31,61,238],"receiver.":[19],"The":[20],"receiver":[21],"utilizes":[22,235],"these":[23],"infer":[26],"time-varying":[28],"targets.":[29],"Due":[30],"limited":[32],"resources,":[34],"the":[35,48,56,74,79,100,108,116,123,152,164,179,187,191,203,208,211,215,219,249,263],"delivered":[36],"may":[38],"not":[39],"be":[40],"fresh.":[41],"To":[42,54,142],"quantify":[43],"data":[44],"freshness,":[45],"we":[46,59,145,197,228],"employ":[47],"Age":[49],"of":[50,73,111,118,178,186,193,210,221,251,265],"Information":[51],"(AoI)":[52],"metric.":[53],"minimize":[55],"error,":[58],"aim":[60],"design":[62],"signal-agnostic":[64],"scheduling":[65,83,170],"policy":[66],"that":[67,150,206,234],"leverages":[68],"AoI":[69,117],"without":[70],"requiring":[71],"knowledge":[72,177,185],"actual":[75],"target":[76],"values":[77],"or":[78],"source":[80,113,157],"observations.":[81],"This":[82],"problem":[84,91,124],"is":[85],"restless":[87],"multi-armed":[88],"bandit":[89,236],"(RMAB)":[90],"with":[92],"non-separable":[94],"penalty":[95,109,153,180,188,195,223],"function.":[96],"Unlike":[97],"traditional":[98,139],"RMABs,":[99],"correlation":[101,212],"among":[102],"introduces":[104],"unique":[106],"challenge:":[107],"function":[110,154],"each":[112,156],"depends":[114],"on":[115,163,202],"other":[119],"sources,":[121],"preventing":[122],"from":[125],"decomposing":[126],"into":[127],"independent":[129],"Markov":[130],"Decision":[131],"Processes":[132],"(MDPs),":[133],"key":[135],"step":[136],"in":[137,255,262],"applying":[138],"RMAB":[140],"solutions.":[141],"address":[143],"this,":[144],"propose":[146],"novel":[148],"approach":[149,233],"approximates":[151],"for":[155,172],"and":[158,182,214,225,259],"establishes":[159],"an":[160,199,230,240],"analytical":[161],"bound":[162,201],"approximation":[165],"error.":[166],"then":[168],"develop":[169,229],"policies":[171,254],"two":[173],"scenarios:":[174],"(i)":[175],"full":[176],"functions":[181,224],"(ii)":[183],"no":[184],"functions.":[189],"For":[190,218],"case":[192,220],"known":[194],"functions,":[196],"present":[198],"upper":[200],"optimality":[204],"gap":[205],"highlights":[207],"impact":[209],"parameter":[213],"size.":[217],"unknown":[222],"signal":[226],"distributions,":[227],"online":[231,241],"learning":[232],"feedback":[237],"learn":[239],"Maximum":[242],"Gain":[243],"First":[244],"policy.":[245],"Simulation":[246],"results":[247],"demonstrate":[248],"effectiveness":[250],"our":[252],"proposed":[253],"minimizing":[256],"error":[258],"achieving":[260],"scalability":[261],"number":[264],"sources.":[266]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-17T00:00:00"}
