{"id":"https://openalex.org/W2957740943","doi":"https://doi.org/10.1109/icc.2019.8761726","title":"Distributed Successive Measurement Selection Based on Online Sparsity Inference","display_name":"Distributed Successive Measurement Selection Based on Online Sparsity Inference","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2957740943","doi":"https://doi.org/10.1109/icc.2019.8761726","mag":"2957740943"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2019.8761726","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2019.8761726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-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/A5101838692","display_name":"Qian Xia","orcid":"https://orcid.org/0000-0002-7786-3336"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qian Xia","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100447180","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-3681-7955"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037101637","display_name":"Rong Ran","orcid":"https://orcid.org/0000-0001-8708-9236"},"institutions":[{"id":"https://openalex.org/I57664883","display_name":"Ajou University","ror":"https://ror.org/03tzb2h73","country_code":"KR","type":"education","lineage":["https://openalex.org/I57664883"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Rong Ran","raw_affiliation_strings":["School of Electrical and Computer Engineering, Ajou University, Suwon, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Ajou University, Suwon, South Korea","institution_ids":["https://openalex.org/I57664883"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100687951","display_name":"Yi Gong","orcid":"https://orcid.org/0000-0001-7392-8991"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Gong","raw_affiliation_strings":["Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100751311","display_name":"Zhaoyang Zhang","orcid":"https://orcid.org/0000-0003-2346-6228"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoyang Zhang","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101838692"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.3537,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62277973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9998999834060669,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9998999834060669,"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9958000183105469,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.803960919380188},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6737520694732666},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6437392234802246},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.6265020370483398},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5953884720802307},{"id":"https://openalex.org/keywords/fusion-center","display_name":"Fusion center","score":0.5454450845718384},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.49048087000846863},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.46098077297210693},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4335970878601074},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.42553040385246277},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4111637473106384},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.37961286306381226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29941898584365845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2932779788970947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.803960919380188},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6737520694732666},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6437392234802246},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.6265020370483398},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5953884720802307},{"id":"https://openalex.org/C2781234732","wikidata":"https://www.wikidata.org/wiki/Q943505","display_name":"Fusion center","level":4,"score":0.5454450845718384},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.49048087000846863},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.46098077297210693},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4335970878601074},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42553040385246277},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4111637473106384},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37961286306381226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29941898584365845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2932779788970947},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc.2019.8761726","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2019.8761726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W78749393","https://openalex.org/W1525535255","https://openalex.org/W1566130651","https://openalex.org/W1964077898","https://openalex.org/W1996215314","https://openalex.org/W2041785419","https://openalex.org/W2068849277","https://openalex.org/W2072517002","https://openalex.org/W2076928311","https://openalex.org/W2108924122","https://openalex.org/W2156929696","https://openalex.org/W2169207653","https://openalex.org/W2257439149","https://openalex.org/W2310779760","https://openalex.org/W2668214543","https://openalex.org/W2741863460","https://openalex.org/W4206180546","https://openalex.org/W4250589301","https://openalex.org/W4285719527","https://openalex.org/W7045698518"],"related_works":["https://openalex.org/W2000147971","https://openalex.org/W1558339915","https://openalex.org/W2066353259","https://openalex.org/W2734417830","https://openalex.org/W2349308680","https://openalex.org/W2060761112","https://openalex.org/W413436171","https://openalex.org/W2407375987","https://openalex.org/W3049691116","https://openalex.org/W2505726097"],"abstract_inverted_index":{"Considering":[0],"the":[1,7,19,30,37,48,53,59,69,73,91,97,101,106,114,128,133,145,150,156,167],"limitations":[2],"on":[3,105],"communication":[4],"capability":[5],"in":[6,17,87],"big":[8],"data":[9,28,55,95,112,119],"era,":[10],"measurement":[11,39,134,162],"selection":[12,40,135,163],"plays":[13],"an":[14],"important":[15,117],"role":[16],"obtaining":[18],"desired":[20],"information":[21,147],"by":[22,96,110,149],"collecting":[23,77],"only":[24],"a":[25,44,64,81],"part":[26],"of":[27,76,93,131,159],"from":[29,52,100],"sensors.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,62],"study":[36],"large-scale":[38],"problem,":[41],"and":[42],"propose":[43,63],"distributed":[45],"algorithm":[46,136],"exploiting":[47],"sparsity":[49,107],"property":[50,108],"extracted":[51],"on-line":[54,98,111],"processing.":[56],"Different":[57],"to":[58,67,89,124,127,144,166],"existing":[60],"works,":[61],"mission-oriented":[65],"framework":[66],"analyze":[68],"performance":[70,157],"improvements":[71],"for":[72],"specific":[74],"mission":[75],"new":[78],"data.":[79,103],"Specifically,":[80],"Bayesian":[82],"hierarchical":[83],"prior":[84],"is":[85,137],"adopted":[86],"order":[88],"quantify":[90],"importance":[92],"uncollected":[94,118],"inference":[99],"collected":[102],"Based":[104],"obtained":[109],"processing,":[113],"sensors":[115],"with":[116],"will":[120],"have":[121],"high":[122],"priority":[123],"access.":[125],"Due":[126],"massive":[129],"number":[130],"sensors,":[132],"executed":[138],"distributively":[139],"at":[140],"each":[141],"device":[142],"according":[143],"common":[146],"broadcast":[148],"fusion":[151],"center.":[152],"Simulation":[153],"results":[154],"demonstrate":[155],"gain":[158],"our":[160],"proposed":[161],"method":[164],"compared":[165],"conventional":[168],"schemes.":[169]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}