{"id":"https://openalex.org/W3162900713","doi":"https://doi.org/10.1145/3409334.3452044","title":"Testbed development for a novel approach towards high accuracy indoor localization with smartphones","display_name":"Testbed development for a novel approach towards high accuracy indoor localization with smartphones","publication_year":2021,"publication_date":"2021-04-15","ids":{"openalex":"https://openalex.org/W3162900713","doi":"https://doi.org/10.1145/3409334.3452044","mag":"3162900713"},"language":"en","primary_location":{"id":"doi:10.1145/3409334.3452044","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409334.3452044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Southeast 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/A5005143098","display_name":"Yunshu Wang","orcid":"https://orcid.org/0000-0003-0558-4132"},"institutions":[{"id":"https://openalex.org/I368840534","display_name":"University of Mississippi","ror":"https://ror.org/02teq1165","country_code":"US","type":"education","lineage":["https://openalex.org/I368840534","https://openalex.org/I4210141039"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yunshu Wang","raw_affiliation_strings":["University of Mississippi"],"affiliations":[{"raw_affiliation_string":"University of Mississippi","institution_ids":["https://openalex.org/I368840534"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069832276","display_name":"Lee Easson","orcid":null},"institutions":[{"id":"https://openalex.org/I134113660","display_name":"University of Nevada, Reno","ror":"https://ror.org/01keh0577","country_code":"US","type":"education","lineage":["https://openalex.org/I134113660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lee Easson","raw_affiliation_strings":["University of Nevada"],"affiliations":[{"raw_affiliation_string":"University of Nevada","institution_ids":["https://openalex.org/I134113660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100431183","display_name":"Feng Wang","orcid":"https://orcid.org/0000-0002-0461-6940"},"institutions":[{"id":"https://openalex.org/I368840534","display_name":"University of Mississippi","ror":"https://ror.org/02teq1165","country_code":"US","type":"education","lineage":["https://openalex.org/I368840534","https://openalex.org/I4210141039"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Wang","raw_affiliation_strings":["University of Mississippi"],"affiliations":[{"raw_affiliation_string":"University of Mississippi","institution_ids":["https://openalex.org/I368840534"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005143098"],"corresponding_institution_ids":["https://openalex.org/I368840534"],"apc_list":null,"apc_paid":null,"fwci":0.2005,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49142802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"86"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/testbed","display_name":"Testbed","score":0.9734369516372681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7881542444229126},{"id":"https://openalex.org/keywords/beacon","display_name":"Beacon","score":0.611175537109375},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.588515043258667},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5867180228233337},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.5128410458564758},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.4560553729534149},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.45509597659111023},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4472547173500061},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41541314125061035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38448911905288696},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1278439462184906}],"concepts":[{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.9734369516372681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7881542444229126},{"id":"https://openalex.org/C102168758","wikidata":"https://www.wikidata.org/wiki/Q7321258","display_name":"Beacon","level":2,"score":0.611175537109375},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.588515043258667},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5867180228233337},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.5128410458564758},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.4560553729534149},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.45509597659111023},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4472547173500061},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41541314125061035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38448911905288696},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1278439462184906},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3409334.3452044","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409334.3452044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Southeast Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1566405224","https://openalex.org/W1890698066","https://openalex.org/W1977356379","https://openalex.org/W2044831755","https://openalex.org/W2546985209","https://openalex.org/W2554376373","https://openalex.org/W2759211418","https://openalex.org/W2782546940","https://openalex.org/W2782845509","https://openalex.org/W2799429135","https://openalex.org/W2808587657","https://openalex.org/W2990396425","https://openalex.org/W2997963616","https://openalex.org/W2998687972","https://openalex.org/W3012315224","https://openalex.org/W4205666304","https://openalex.org/W4238989166","https://openalex.org/W4240802945","https://openalex.org/W4285581227"],"related_works":["https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W2790129070","https://openalex.org/W3003320923","https://openalex.org/W2106845956","https://openalex.org/W3158435160","https://openalex.org/W2112938119","https://openalex.org/W2992410632","https://openalex.org/W2768717251","https://openalex.org/W2025756212"],"abstract_inverted_index":{"Due":[0],"to":[1,26,180,204,215],"its":[2],"deep":[3,94,211],"penetration":[4],"in":[5,224],"people's":[6],"daily":[7],"life,":[8],"smartphone":[9],"has":[10],"been":[11,55],"proposed":[12],"as":[13,48,210],"a":[14,43,89,201],"practical":[15,107,173],"platform":[16],"for":[17,105,155,169],"indoor":[18,99],"localization.":[19],"Yet":[20],"one":[21,79],"major":[22],"challenge":[23,85],"is":[24],"how":[25],"handle":[27],"the":[28,97,103,116,125,146,192],"non-negligible":[29],"sensor":[30,140,218],"errors":[31,141,219],"that":[32,91,188],"can":[33,66,92,165],"become":[34],"problematic":[35],"when":[36],"accumulated":[37],"over":[38],"time.":[39],"To":[40],"this":[41,75,84],"end,":[42],"series":[44],"of":[45,139,194],"approaches":[46,214],"such":[47,209],"fingerprint":[49],"and":[50,102,121,130,134,142,157,162,172,184,220],"pedestrian":[51],"dead":[52],"reckoning":[53],"have":[54,176],"proposed,":[56],"which,":[57],"however,":[58],"either":[59],"need":[60],"WiFi":[61],"infrastructure,":[62],"pre-installed":[63],"beacons":[64],"or":[65,72],"only":[67,114,190],"support":[68],"certain":[69],"movement":[70],"patterns":[71],"scenarios.":[73],"In":[74,110],"paper,":[76],"we":[77],"take":[78],"step":[80],"further":[81],"towards":[82],"tackle":[83],"by":[86],"carefully":[87],"developing":[88],"testbed":[90,113,150,196],"enable":[93],"investigation":[95],"on":[96,145],"smartphone-based":[98],"localization":[100,147,156],"problem":[101],"potential":[104],"promising":[106],"solution":[108,170],"design.":[109],"particular,":[111],"our":[112,182,195],"accesses":[115],"raw":[117],"inertial":[118],"measurement":[119],"unit":[120],"orientation":[122],"data":[123,160],"from":[124],"smartphone,":[126],"making":[127],"it":[128],"infrastructure-free":[129],"require":[131],"no":[132],"pre-installation,":[133],"providing":[135],"an":[136],"in-depth":[137],"view":[138],"their":[143],"impacts":[144],"accuracy.":[148],"Our":[149],"also":[151,199],"provides":[152],"built-in":[153],"functionalities":[154],"supports":[158],"real-time":[159],"processing":[161],"visualization,":[163],"which":[164],"be":[166],"extremely":[167],"valuable":[168],"development":[171],"usefulness.":[174],"We":[175],"conducted":[177],"extensive":[178],"experiments":[179],"evaluate":[181],"testbed,":[183],"obtained":[185],"interesting":[186],"observations":[187],"not":[189],"validate":[191],"effectiveness":[193],"design,":[197],"but":[198],"opens":[200],"future":[202],"direction":[203],"develop":[205],"more":[206],"advanced":[207],"mechanisms":[208],"learning":[212],"based":[213],"better":[216],"compensate":[217],"achieve":[221],"high":[222],"accuracy":[223],"practice.":[225]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
