{"id":"https://openalex.org/W2807923355","doi":"https://doi.org/10.1109/jiot.2018.2847697","title":"A Dynamic Bayesian Nonparametric Model for Blind Calibration of Sensor Networks","display_name":"A Dynamic Bayesian Nonparametric Model for Blind Calibration of Sensor Networks","publication_year":2018,"publication_date":"2018-06-15","ids":{"openalex":"https://openalex.org/W2807923355","doi":"https://doi.org/10.1109/jiot.2018.2847697","mag":"2807923355"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2018.2847697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2018.2847697","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://dr.ntu.edu.sg/bitstream/10356/102693/1/YanZhoTay%20-%20A%20dynamic%20Bayesian%20nonparametric%20model%20for%20blind%20calibration%20of%20sensor%20networks.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018463611","display_name":"Jielong Yang","orcid":"https://orcid.org/0000-0001-5853-6316"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jielong Yang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-5853-6316","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681425","display_name":"Xionghu Zhong","orcid":"https://orcid.org/0000-0002-7533-2347"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xionghu Zhong","raw_affiliation_strings":["School of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046850399","display_name":"Wee Peng Tay","orcid":"https://orcid.org/0000-0002-1543-195X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wee Peng Tay","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-1543-195X","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4569,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.91480391,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"5","issue":"5","first_page":"3942","last_page":"3953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7325384020805359},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6710835695266724},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5440851449966431},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5422244668006897},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5354155898094177},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5213322043418884},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5065590143203735},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4902060627937317},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4794415533542633},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.46994975209236145},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46795332431793213},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.45413124561309814},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4189611077308655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35760170221328735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1828063428401947},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14129206538200378},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.09079310297966003},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.080272376537323}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325384020805359},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6710835695266724},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5440851449966431},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5422244668006897},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5354155898094177},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5213322043418884},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5065590143203735},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4902060627937317},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4794415533542633},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.46994975209236145},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46795332431793213},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.45413124561309814},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4189611077308655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35760170221328735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1828063428401947},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14129206538200378},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.09079310297966003},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.080272376537323},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jiot.2018.2847697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2018.2847697","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/102693","is_oa":true,"landing_page_url":"http://hdl.handle.net/10220/47842","pdf_url":"https://dr.ntu.edu.sg/bitstream/10356/102693/1/YanZhoTay%20-%20A%20dynamic%20Bayesian%20nonparametric%20model%20for%20blind%20calibration%20of%20sensor%20networks.pdf","source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"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":"Journal Article"}],"best_oa_location":{"id":"pmh:oai:dr.ntu.edu.sg:10356/102693","is_oa":true,"landing_page_url":"http://hdl.handle.net/10220/47842","pdf_url":"https://dr.ntu.edu.sg/bitstream/10356/102693/1/YanZhoTay%20-%20A%20dynamic%20Bayesian%20nonparametric%20model%20for%20blind%20calibration%20of%20sensor%20networks.pdf","source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"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":"Journal Article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320318804","display_name":"Delta Electronics","ror":"https://ror.org/04s3g5933"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2807923355.pdf","grobid_xml":"https://content.openalex.org/works/W2807923355.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W178056938","https://openalex.org/W1503398984","https://openalex.org/W1785920177","https://openalex.org/W1805434785","https://openalex.org/W1965324089","https://openalex.org/W2007641398","https://openalex.org/W2011760672","https://openalex.org/W2012352536","https://openalex.org/W2041507040","https://openalex.org/W2049683924","https://openalex.org/W2051378923","https://openalex.org/W2071079847","https://openalex.org/W2071975411","https://openalex.org/W2074719413","https://openalex.org/W2080972498","https://openalex.org/W2102662752","https://openalex.org/W2109646819","https://openalex.org/W2115245044","https://openalex.org/W2115870554","https://openalex.org/W2118036030","https://openalex.org/W2126841536","https://openalex.org/W2130363011","https://openalex.org/W2136525316","https://openalex.org/W2137997529","https://openalex.org/W2138863982","https://openalex.org/W2142243993","https://openalex.org/W2146414958","https://openalex.org/W2150679233","https://openalex.org/W2151375094","https://openalex.org/W2152139894","https://openalex.org/W2158266063","https://openalex.org/W2160337655","https://openalex.org/W2165232124","https://openalex.org/W2171092999","https://openalex.org/W2403489609","https://openalex.org/W2462796524","https://openalex.org/W2560308067","https://openalex.org/W2587254582","https://openalex.org/W2714335099","https://openalex.org/W2963883852","https://openalex.org/W3104490327","https://openalex.org/W4237127183","https://openalex.org/W6638015808","https://openalex.org/W6677407863","https://openalex.org/W6679627699","https://openalex.org/W6682754680","https://openalex.org/W6824062597"],"related_works":["https://openalex.org/W3125971950","https://openalex.org/W1580681286","https://openalex.org/W2175355783","https://openalex.org/W2622204791","https://openalex.org/W1579866848","https://openalex.org/W1546022168","https://openalex.org/W2066716418","https://openalex.org/W2905524938","https://openalex.org/W3198356641","https://openalex.org/W2071668645"],"abstract_inverted_index":{"We":[0,44,70,102],"consider":[1],"the":[2,12,36,52,63,79,97,121,184,190],"problem":[3],"of":[4,7,22],"blind":[5,170],"calibration":[6,171],"a":[8,29,55,72,100,112,151,174],"sensor":[9,13,40,64,88,98,122,185],"network,":[10],"where":[11],"gains":[14,65,81,191],"and":[15,66,82,111,125,143,154,179,187,192],"offsets":[16,67,126],"are":[17,42,68],"estimated":[18],"from":[19],"noisy":[20],"observations":[21,41,186],"unknown":[23,93],"signals.":[24],"This":[25],"is":[26],"in":[27],"general":[28],"nonidentifiable":[30],"problem,":[31],"unless":[32],"restrictive":[33],"assumptions":[34],"on":[35,140,156],"signal":[37,49],"subspace":[38],"or":[39],"imposed.":[43],"show":[45],"that":[46,119,161,181],"if":[47],"each":[48],"observed":[50],"by":[51],"sensors":[53],"follows":[54],"known":[56],"dynamic":[57,73],"model":[58,76,85],"with":[59],"additive":[60],"noise,":[61],"then":[62,188],"identifiable.":[69],"propose":[71,134],"Bayesian":[74,176],"nonparametric":[75],"to":[77,90],"infer":[78],"sensors'":[80],"offsets.":[83,193],"Our":[84],"allows":[86],"different":[87,92],"clusters":[89,99],"observe":[91],"signals,":[94],"without":[95],"knowing":[96],"priori.":[101],"develop":[103],"an":[104,135],"offline":[105],"algorithm":[106,138],"using":[107,150],"block":[108],"Gibbs":[109],"sampling":[110,117],"linearized":[113],"forward":[114],"filtering":[115,142],"backward":[116],"method":[118],"estimates":[120],"clusters,":[123],"gains,":[124],"jointly.":[127],"Furthermore,":[128],"for":[129],"practical":[130],"implementation,":[131],"we":[132],"also":[133],"online":[136],"inference":[137],"based":[139],"particle":[141],"local":[144],"Markov":[145],"chain":[146],"Monte":[147],"Carlo.":[148],"Simulations":[149],"synthetic":[152],"dataset,":[153],"experiments":[155],"two":[157],"real":[158],"datasets":[159],"suggest":[160],"our":[162],"proposed":[163],"methods":[164,180],"perform":[165],"better":[166],"than":[167],"several":[168],"other":[169],"methods,":[172],"including":[173],"sparse":[175],"learning":[177],"approach,":[178],"first":[182],"cluster":[183],"estimate":[189]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}