{"id":"https://openalex.org/W4414838694","doi":"https://doi.org/10.1109/indin64977.2025.11279431","title":"A GMM-Assisted Learning Approach for Ranging with IEEE802.15.4 devices","display_name":"A GMM-Assisted Learning Approach for Ranging with IEEE802.15.4 devices","publication_year":2025,"publication_date":"2025-07-12","ids":{"openalex":"https://openalex.org/W4414838694","doi":"https://doi.org/10.1109/indin64977.2025.11279431"},"language":"en","primary_location":{"id":"doi:10.1109/indin64977.2025.11279431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-05289003","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Manjiang Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]},{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Manjiang Cao","raw_affiliation_strings":["The Hong Kong University of Science and Technology [Guangzhou] (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology [Guangzhou] (China)","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895","https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080136283","display_name":"Thomas Watteyne","orcid":"https://orcid.org/0000-0002-3695-9315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Watteyne","raw_affiliation_strings":["AIO - Mise en r\u00e9seau fiable, sans fil \u00e0 faible consommation et micro-robotique (48 Rue Barrault, 75013 Paris - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AIO - Mise en r\u00e9seau fiable, sans fil \u00e0 faible consommation et micro-robotique (48 Rue Barrault, 75013 Paris - France)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069438439","display_name":"Tengfei Chang","orcid":"https://orcid.org/0000-0001-9589-7794"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]},{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Tengfei Chang","raw_affiliation_strings":["The Hong Kong University of Science and Technology [Guangzhou] (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology [Guangzhou] (China)","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895","https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9876999855041504,"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":0.9876999855041504,"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/T10655","display_name":"GNSS positioning and interference","score":0.9725000262260437,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9451000094413757,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/ranging","display_name":"Ranging","score":0.92330002784729},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5421000123023987},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5023000240325928},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4749000072479248},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4334999918937683},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.3986999988555908},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.3939000070095062},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.3718000054359436},{"id":"https://openalex.org/keywords/received-signal-strength-indication","display_name":"Received signal strength indication","score":0.36579999327659607}],"concepts":[{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.92330002784729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345000267028809},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5421000123023987},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4560999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4334999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43320000171661377},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.3939000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C2778913798","wikidata":"https://www.wikidata.org/wiki/Q1195672","display_name":"Received signal strength indication","level":3,"score":0.36579999327659607},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3361000120639801},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3336000144481659},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/indin64977.2025.11279431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-05289003v1","is_oa":true,"landing_page_url":"https://hal.science/hal-05289003","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"INDIN 2025 - 23rd IEEE International Conference on Industrial Informatics, Jul 2025, KunMing, China","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-05289003v1","is_oa":true,"landing_page_url":"https://hal.science/hal-05289003","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"INDIN 2025 - 23rd IEEE International Conference on Industrial Informatics, Jul 2025, KunMing, China","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"IEEE":[0,104,192],"802.15.4":[1,105,193],"is":[2,38,66],"the":[3,50,59,63,87,97,139,152,157,171,180,183,225],"core":[4],"of":[5,62,99,123,137,144,156,229,262],"multiple":[6],"industrial":[7],"standards":[8],"such":[9,73],"as":[10,41,74,170],"WirelessHART,":[11],"ISA100.11a,":[12],"and":[13,19,93,112,142,148,154,163,185,207,234,267],"6TiSCH.":[14],"It":[15],"provides":[16],"ultra-low-power":[17],"operation":[18],"high":[20],"reliability":[21],"for":[22,68,107],"battery-powered":[23],"devices":[24,194],"requiring":[25],"a":[26,42,119,129,174,248,253],"5-10":[27],"year":[28],"lifetime.":[29],"However,":[30],"due":[31],"to":[32,56,85,173,178],"its":[33],"narrow":[34],"frequency":[35],"bandwidth,":[36],"it":[37,54],"rarely":[39],"used":[40,169],"wireless":[43],"ranging":[44,226],"technique.":[45],"Its":[46],"low":[47],"resolution":[48],"in":[49,195,242],"time":[51,61],"domain":[52],"makes":[53],"difficult":[55],"precisely":[57],"determine":[58],"arrival":[60],"signal,":[64],"which":[65],"essential":[67],"Time-of-Flight":[69],"ranging.":[70],"Emerging":[71],"approaches,":[72],"WiFi":[75],"fingerprinting,":[76],"show":[77,213],"promising":[78],"localization":[79],"performance":[80],"by":[81],"using":[82,100,191,219,252],"learning":[83,101],"methods":[84],"find":[86],"pattern":[88],"mapping":[89],"between":[90,182],"signal":[91],"characteristics":[92],"positions.This":[94],"paper":[95],"explores":[96],"potential":[98],"approaches":[102],"over":[103],"radios":[106],"ranging,":[108],"based":[109],"on":[110],"RSSI":[111],"Round":[113],"Trip":[114],"Timing":[115],"(RTT)":[116],"samples.":[117],"As":[118],"preprocessing":[120],"step,":[121],"groups":[122],"RTT":[124,164],"samples":[125],"are":[126,168,240,273],"fed":[127],"into":[128],"Gaussian":[130],"Mixture":[131],"Model":[132],"(GMM),":[133],"extracting":[134],"two":[135,145],"sets":[136],"features:":[138],"mean,":[140],"variance,":[141],"weight":[143],"distributions":[146],"(line-of-sight":[147],"non-line-of-sight).":[149],"Together":[150],"with":[151,215],"mean":[153],"variance":[155],"Received":[158],"Signal":[159],"Strength":[160],"Indicator":[161],"(RSSI)":[162],"samples,":[165],"10":[166],"features":[167],"input":[172],"multilayer":[175],"perceptron":[176],"network":[177],"predict":[179],"distance":[181],"requester":[184],"reflector.The":[186],"approach":[187],"has":[188],"been":[189],"evaluated":[190],"three":[196,244,258],"different":[197],"scenarios:":[198],"an":[199,203,208],"empty":[200],"football":[201],"field,":[202],"underground":[204],"parking":[205],"lot,":[206],"office":[209],"area.":[210],"The":[211],"results":[212],"that":[214],"dedicated":[216],"models":[217],"trained":[218,251],"datasets":[220],"collected":[221],"from":[222,256],"each":[223],"scenario,":[224],"prediction":[227,260],"errors":[228,261],"0.25":[230],"meters,":[231,233,264,266],"0.42":[232],"0.58":[235],"meters":[236,269],"at":[237,270],"80%":[238,271],"probability":[239,272],"achieved":[241],"these":[243],"scenarios,":[245,259],"respectively.":[246],"With":[247],"single":[249],"model":[250],"combined":[254],"dataset":[255],"all":[257],"0.41":[263],"0.60":[265],"0.97":[268],"achieved.":[274]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-06T00:00:00"}