{"id":"https://openalex.org/W7124905372","doi":"https://doi.org/10.1109/tccn.2026.3656287","title":"Probabilistic Spatio-Temporal Data Inference in Sparse Mobile Crowdsensing With Conditional Diffusion Model","display_name":"Probabilistic Spatio-Temporal Data Inference in Sparse Mobile Crowdsensing With Conditional Diffusion Model","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7124905372","doi":"https://doi.org/10.1109/tccn.2026.3656287"},"language":null,"primary_location":{"id":"doi:10.1109/tccn.2026.3656287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3656287","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and 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":"https://openalex.org/A5103144170","display_name":"Hu He","orcid":"https://orcid.org/0009-0003-6841-1754"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hu He","raw_affiliation_strings":["School of Computer Science and Engineering, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jun Peng","orcid":"https://orcid.org/0000-0001-6269-6929"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Peng","raw_affiliation_strings":["School of Electronic Information, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lin Cai","orcid":"https://orcid.org/0000-0002-1093-4865"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Lin Cai","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049066561","display_name":"Weirong Liu","orcid":"https://orcid.org/0000-0002-6207-9100"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weirong Liu","raw_affiliation_strings":["School of Computer Science and Engineering, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhiwu Huang","orcid":"https://orcid.org/0000-0002-5485-2562"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwu Huang","raw_affiliation_strings":["School of Automation, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103144170"],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10540502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"5595","last_page":"5608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9764999747276306,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9764999747276306,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.01269999984651804,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.001500000013038516,"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/inference","display_name":"Inference","score":0.6466000080108643},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5616000294685364},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5090000033378601},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5085999965667725},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.40860000252723694},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.40560001134872437},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.40540000796318054},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4023999869823456},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.387800008058548},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3824999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8449000120162964},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6466000080108643},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5554999709129333},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5085999965667725},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.40860000252723694},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39959999918937683},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3824999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38179999589920044},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tccn.2026.3656287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3656287","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7534705996513367}],"awards":[{"id":"https://openalex.org/G2142754265","display_name":null,"funder_award_id":"62172448","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2125826911","https://openalex.org/W2343462218","https://openalex.org/W2473808492","https://openalex.org/W2593971128","https://openalex.org/W2791626981","https://openalex.org/W2949041559","https://openalex.org/W2964015378","https://openalex.org/W3003381204","https://openalex.org/W3130452720","https://openalex.org/W3171737424","https://openalex.org/W4294068620","https://openalex.org/W4295665208","https://openalex.org/W4300006734","https://openalex.org/W4311415873","https://openalex.org/W4312933868","https://openalex.org/W4367057034","https://openalex.org/W4385245566","https://openalex.org/W4385270120","https://openalex.org/W4385562623","https://openalex.org/W4386260613","https://openalex.org/W4387490191","https://openalex.org/W4387682310","https://openalex.org/W4389352638","https://openalex.org/W4391097131","https://openalex.org/W4395030196","https://openalex.org/W4395069553","https://openalex.org/W4396821794","https://openalex.org/W4398152831","https://openalex.org/W4399311140","https://openalex.org/W4400112617","https://openalex.org/W4404627973","https://openalex.org/W4406521366","https://openalex.org/W4407408304","https://openalex.org/W4414348312"],"related_works":[],"abstract_inverted_index":{"Mobile":[0],"crowdsensing":[1],"(MCS)":[2],"is":[3,133],"a":[4,43,88,129,136,145,162],"data":[5,23,44,100,116,143],"sensing":[6,22,71,115,142,184,199],"paradigm":[7],"that":[8,77,150,166],"recruits":[9],"mobile":[10],"users":[11],"with":[12,135],"smart":[13],"devices":[14],"to":[15,31,47,65,111,194],"collect":[16],"data.":[17,55],"However,":[18],"the":[19,67,105,172,187,190],"collected":[20],"spatio-temporal":[21,97,152],"in":[24,70],"real-world":[25,182],"scenarios":[26],"are":[27],"often":[28],"incomplete":[29],"due":[30],"participants":[32],"availability":[33],"or":[34],"transmission":[35],"loss.":[36],"Sparse":[37],"MCS":[38,99],"has":[39],"been":[40],"proposed":[41,191],"as":[42,157],"inference":[45,126],"technology":[46],"infer":[48],"values":[49],"for":[50,95,114,140],"unsensed":[51],"regions":[52],"using":[53,171],"sensed":[54],"Existing":[56],"approaches":[57],"typically":[58],"rely":[59],"on":[60,180],"deterministic":[61],"inference,":[62],"which":[63],"struggles":[64],"capture":[66],"inherent":[68],"uncertainty":[69,117],"data,":[72],"and":[73,118,124,154,161,201],"employ":[74],"autoregressive":[75],"methods":[76],"suffer":[78],"from":[79],"error":[80,120],"accumulation.":[81],"To":[82],"address":[83],"these":[84],"issues,":[85],"we":[86],"propose":[87],"conditional":[89,146],"diffusion":[90,109],"probabilistic":[91],"framework":[92],"named":[93],"Diffusion-MCS":[94],"efficient":[96],"sparse":[98],"inference.":[101],"First,":[102],"it":[103],"leverages":[104],"generative":[106],"power":[107],"of":[108,189],"models":[110],"explicitly":[112],"account":[113],"alleviate":[119],"accumulation":[121],"through":[122],"iterative":[123],"non-autoregressive":[125],"refinement.":[127],"Then,":[128],"noise":[130,163],"prediction":[131],"model":[132],"designed":[134],"learnable":[137],"representation":[138],"module":[139,149,165],"coarse-grained":[141],"interpolation,":[144],"feature":[147],"extraction":[148],"captures":[151],"correlations":[153],"geographical":[155],"dependencies":[156],"global":[158],"context":[159],"features,":[160],"estimation":[164],"iteratively":[167],"refines":[168],"noisy":[169],"samples":[170],"extracted":[173],"features":[174],"via":[175],"cross-attention":[176],"mechanisms.":[177],"Extensive":[178],"experiments":[179],"four":[181],"urban":[183],"datasets":[185],"demonstrate":[186],"effectiveness":[188],"method":[192],"compared":[193],"seven":[195],"baselines":[196],"across":[197],"various":[198],"ratios":[200],"missing":[202],"patterns.":[203]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-21T00:00:00"}