{"id":"https://openalex.org/W4315630265","doi":"https://doi.org/10.1109/globecom48099.2022.10001732","title":"Reconstruction of Human Trajectories Based on Anonymous Sensor Data for Estimating Infection Route","display_name":"Reconstruction of Human Trajectories Based on Anonymous Sensor Data for Estimating Infection Route","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315630265","doi":"https://doi.org/10.1109/globecom48099.2022.10001732"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10001732","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10001732","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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/A5109441382","display_name":"Kotaro Matsuda","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kotaro Matsuda","raw_affiliation_strings":["NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026184657","display_name":"Hiroki Ikeuchi","orcid":"https://orcid.org/0000-0002-7793-9433"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Ikeuchi","raw_affiliation_strings":["NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104044038","display_name":"Yousuke Takahashi","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yousuke Takahashi","raw_affiliation_strings":["NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072206568","display_name":"Tsuyoshi Toyono","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsuyoshi Toyono","raw_affiliation_strings":["NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Network Service Systems Laboratories, NTT Corporation,Tokyo,Japan,180-8585","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41772152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"1996","last_page":"2001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"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/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.9973999857902527,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9900000095367432,"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/computer-science","display_name":"Computer science","score":0.7302597761154175},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7283586263656616},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5636265873908997},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.5594245195388794},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5591515898704529},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5432130694389343},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.4937973916530609},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.48780491948127747},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4694136083126068},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4512162208557129},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4046747088432312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2598482072353363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.217603862285614},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.18743181228637695},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1411016881465912},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13317999243736267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7302597761154175},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7283586263656616},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5636265873908997},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.5594245195388794},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5591515898704529},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5432130694389343},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.4937973916530609},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.48780491948127747},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4694136083126068},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4512162208557129},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4046747088432312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2598482072353363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.217603862285614},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.18743181228637695},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1411016881465912},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13317999243736267},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10001732","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10001732","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1626398438","https://openalex.org/W1964461063","https://openalex.org/W1977545325","https://openalex.org/W1977555135","https://openalex.org/W2084224084","https://openalex.org/W2136317921","https://openalex.org/W2140251882","https://openalex.org/W2166771065","https://openalex.org/W2398554122","https://openalex.org/W2523438800","https://openalex.org/W2768256553","https://openalex.org/W3036422000","https://openalex.org/W3099552904","https://openalex.org/W4299734264","https://openalex.org/W6636500457","https://openalex.org/W6992951189"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2364370872","https://openalex.org/W2097963413","https://openalex.org/W2294335174","https://openalex.org/W2025614924","https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W2367222340"],"abstract_inverted_index":{"The":[0],"COVID-19":[1],"Pandemic":[2],"has":[3],"increased":[4],"the":[5,13,41,53,91,117,121,125,148],"demands":[6],"of":[7,15,44,55,75,93,120,127,150],"governments":[8],"for":[9,86,111],"technologies":[10],"to":[11,39],"estimate":[12],"route":[14],"infection.":[16],"In":[17],"this":[18],"paper,":[19],"we":[20],"propose":[21,82],"a":[22,69,73,83,104,141],"new":[23,84],"smart":[24,70],"city":[25,71],"framework":[26],"that":[27,100],"collects":[28],"anonymized":[29,56,94],"passage":[30,57,60,63,95],"information":[31],"from":[32],"deployed":[33],"Bluetooth":[34],"sensors":[35,67],"and":[36,62,107,116,158],"analyzes":[37],"them":[38],"reconstruct":[40],"multiple":[42,50,88,134],"trajectories":[43,51,89,135],"infected":[45],"people.":[46],"We":[47,81,146],"formulate":[48],"recovering":[49],"on":[52,90],"basis":[54,92,126],"information,":[58],"including":[59],"time":[61,110],"position,":[64],"obtained":[65],"by":[66,139],"in":[68,78,114,153],"as":[72],"problem":[74],"multiple-trajectory":[76],"reconstruction":[77],"general":[79],"networks.":[80],"method":[85,98,131,152],"reconstructing":[87],"information.":[96],"Our":[97],"assumes":[99],"each":[101,112],"trajectory":[102,161],"follows":[103],"Markov":[105,122],"process":[106],"estimates":[108],"transit":[109],"edge":[113],"networks":[115],"transition":[118],"probability":[119],"process.":[123],"On":[124],"its":[128],"estimation,":[129],"our":[130,151],"can":[132],"find":[133],"with":[136],"maximum":[137],"likelihood":[138],"solving":[140],"minimum":[142],"cost":[143],"flow":[144],"problem.":[145],"evaluate":[147],"performance":[149],"experiments":[154],"using":[155],"simulation":[156],"data":[157],"actual":[159],"human":[160],"data.":[162]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}