{"id":"https://openalex.org/W7117578025","doi":"https://doi.org/10.1109/mswim67937.2025.11308743","title":"Privacy-Preserving Device Counting Using Wi-Fi Channel State Information and Deep Learning","display_name":"Privacy-Preserving Device Counting Using Wi-Fi Channel State Information and Deep Learning","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7117578025","doi":"https://doi.org/10.1109/mswim67937.2025.11308743"},"language":null,"primary_location":{"id":"doi:10.1109/mswim67937.2025.11308743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mswim67937.2025.11308743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5002122040","display_name":"Pegah Torkamandi","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pegah Torkamandi","raw_affiliation_strings":["Technical University of Munich,Munich,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Munich,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091138244","display_name":"Navid Hasanzadeh","orcid":"https://orcid.org/0000-0002-1253-4355"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Navid Hasanzadeh","raw_affiliation_strings":["University of Toronto,Toronto,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto,Toronto,Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027075213","display_name":"J\u00f6rg Ott","orcid":"https://orcid.org/0000-0001-8311-8036"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J\u00f6rg Ott","raw_affiliation_strings":["Technical University of Munich,Munich,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Munich,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089951737","display_name":"Shahrokh Valaee","orcid":"https://orcid.org/0000-0001-6254-1660"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shahrokh Valaee","raw_affiliation_strings":["University of Toronto,Toronto,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto,Toronto,Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":"74","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.2800000011920929,"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":0.2800000011920929,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.26109999418258667,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.22349999845027924,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7820000052452087},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.6550999879837036},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5582000017166138},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5343999862670898},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4912000000476837},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42100000381469727},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4050000011920929},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.38589999079704285},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.36039999127388}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8070999979972839},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7820000052452087},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.6550999879837036},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5582000017166138},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4645000100135803},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32019999623298645},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3190999925136566},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C2777321455","wikidata":"https://www.wikidata.org/wiki/Q20484","display_name":"MAC address","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C121449826","wikidata":"https://www.wikidata.org/wiki/Q864114","display_name":"Input device","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mswim67937.2025.11308743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mswim67937.2025.11308743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7825736999511719,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1983301216","https://openalex.org/W2035883018","https://openalex.org/W2089695767","https://openalex.org/W2111406701","https://openalex.org/W2158698691","https://openalex.org/W2170505850","https://openalex.org/W2394784643","https://openalex.org/W2411034138","https://openalex.org/W2476314115","https://openalex.org/W2734845228","https://openalex.org/W2783524404","https://openalex.org/W2896714260","https://openalex.org/W2949676527","https://openalex.org/W2950099298","https://openalex.org/W2952065976","https://openalex.org/W2963562852","https://openalex.org/W2980021985","https://openalex.org/W2990026991","https://openalex.org/W3005904310","https://openalex.org/W3023135330","https://openalex.org/W3086779792","https://openalex.org/W3145376091","https://openalex.org/W3196328959","https://openalex.org/W3203019240","https://openalex.org/W4200307562","https://openalex.org/W4205127651","https://openalex.org/W4214813322","https://openalex.org/W4254751698","https://openalex.org/W4306763719","https://openalex.org/W4307823382","https://openalex.org/W4392126269","https://openalex.org/W4396575164","https://openalex.org/W4403759251","https://openalex.org/W4405270170","https://openalex.org/W4410359154","https://openalex.org/W4412030422"],"related_works":[],"abstract_inverted_index":{"As":[0],"smartphones":[1],"and":[2,93,165,180,188,202,213],"other":[3,37],"Wi-Fi-Enabled":[4],"devices":[5,25,58],"become":[6],"increasingly":[7],"common,":[8],"they":[9],"offer":[10],"a":[11,64,108,146],"practical":[12],"way":[13],"to":[14,46,52,106,155,191],"estimate":[15],"crowd":[16],"size":[17],"by":[18,209],"collecting":[19],"the":[20,40,116,125,130,133],"probe":[21,80],"request":[22],"frames":[23],"these":[24],"periodically":[26],"transmit.":[27],"While":[28],"this":[29],"passive":[30],"approach":[31,67,162],"avoids":[32],"relying":[33],"on":[34],"cameras":[35],"or":[36,97],"intrusive":[38],"sensors,":[39],"adoption":[41],"of":[42,118,132,136],"MAC":[43,142],"address":[44,129],"randomization\u2014designed":[45],"protect":[47],"user":[48],"privacy\u2014makes":[49],"it":[50],"difficult":[51],"reliably":[53],"count":[54],"how":[55],"many":[56],"unique":[57],"are":[59],"present.":[60],"This":[61],"paper":[62],"presents":[63],"machine":[65],"learning\u2013based":[66],"for":[68],"device":[69,86,170,207],"counting":[70,195],"that":[71,150],"leverages":[72,185],"Channel":[73],"State":[74],"Information":[75],"(CSI)":[76],"features":[77],"extracted":[78],"from":[79,124,139],"frames.":[81],"Our":[82],"method":[83],"enables":[84],"accurate":[85],"population":[87],"estimation":[88],"while":[89],"preserving":[90,206],"MAC-level":[91],"privacy":[92,208],"avoiding":[94,210],"persistent":[95],"tracking":[96,212],"behavioral":[98,211],"profiling.":[99],"A":[100],"Siamese":[101],"neural":[102],"network":[103],"is":[104],"trained":[105],"learn":[107],"discriminative":[109],"similarity":[110],"function":[111],"between":[112,152],"packet":[113],"pairs,":[114],"allowing":[115],"grouping":[117],"temporally":[119],"co-occurring":[120],"packets":[121,137],"likely":[122],"originating":[123],"same":[126],"device.":[127],"To":[128],"challenge":[131],"limited":[134],"number":[135],"available":[138],"each":[140],"randomized":[141],"address,":[143],"we":[144],"use":[145],"lightweight":[147],"augmentation":[148],"strategy":[149],"interpolates":[151],"CSI":[153],"samples":[154],"increase":[156],"training":[157],"density.":[158],"We":[159],"evaluate":[160],"our":[161,183],"in":[163,199],"indoor":[164,200],"outdoor":[166],"settings":[167],"with":[168],"varying":[169],"probing":[171],"behaviors":[172],"under":[173],"static":[174],"conditions.":[175],"By":[176],"leveraging":[177],"packet-level":[178,186],"voting":[179,187],"device-counting":[181,189],"strategies,":[182],"model":[184],"strategies":[190],"consistently":[192],"achieve":[193],"high":[194],"accuracy\u2014averaging":[196],"over":[197],"98%":[198],"environments":[201],"reaching":[203],"100%":[204],"outdoors\u2014while":[205],"re-identification.":[214]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-30T00:00:00"}
