{"id":"https://openalex.org/W7160604997","doi":"https://doi.org/10.1145/3774906.3802759","title":"Towards Generalizable Wireless Sensing Models via Pre-training on Multi-Source Datasets","display_name":"Towards Generalizable Wireless Sensing Models via Pre-training on Multi-Source Datasets","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160604997","doi":"https://doi.org/10.1145/3774906.3802759"},"language":null,"primary_location":{"id":"doi:10.1145/3774906.3802759","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774906.3802759","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774906.3802759","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123558106","display_name":"Chen Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Gong","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2888-2370","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135654491","display_name":"Bo Liang","orcid":"https://orcid.org/0000-0001-7226-8178"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Liang","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7226-8178","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133836240","display_name":"Q Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qihao Zhu","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-5155-451X","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006292758","display_name":"Wei Gao","orcid":"https://orcid.org/0000-0003-2144-6960"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Gao","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, USA"],"raw_orcid":"https://orcid.org/0000-0003-2144-6960","affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135702990","display_name":"Yin Chen","orcid":"https://orcid.org/0000-0002-2652-8941"},"institutions":[{"id":"https://openalex.org/I193758575","display_name":"Reitaku University","ror":"https://ror.org/02rwyg032","country_code":"JP","type":"education","lineage":["https://openalex.org/I193758575"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yin Chen","raw_affiliation_strings":["Reitaku University, Kashiwa, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2652-8941","affiliations":[{"raw_affiliation_string":"Reitaku University, Kashiwa, Japan","institution_ids":["https://openalex.org/I193758575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135640602","display_name":"Jin Nakazawa","orcid":"https://orcid.org/0000-0001-9718-4552"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jin Nakazawa","raw_affiliation_strings":["Keio University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-9718-4552","affiliations":[{"raw_affiliation_string":"Keio University, Tokyo, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003999919","display_name":"Chenren Xu","orcid":"https://orcid.org/0000-0001-9171-2596"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenren Xu","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9171-2596","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"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":0.6638721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"488","last_page":"502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.8765000104904175,"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":0.8765000104904175,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.012400000356137753,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.01209999993443489,"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/task","display_name":"Task (project management)","score":0.6798999905586243},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/data-deduplication","display_name":"Data deduplication","score":0.4794999957084656},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4348999857902527},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.39989998936653137},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3921999931335449},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3052999973297119}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8241000175476074},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6798999905586243},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C32587265","wikidata":"https://www.wikidata.org/wiki/Q1182260","display_name":"Data deduplication","level":2,"score":0.4794999957084656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4586000144481659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44350001215934753},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.26179999113082886},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774906.3802759","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774906.3802759","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774906.3802759","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774906.3802759","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1997834106","https://openalex.org/W2025768430","https://openalex.org/W2095396347","https://openalex.org/W2896542121","https://openalex.org/W2963539531","https://openalex.org/W2963781481","https://openalex.org/W2969645632","https://openalex.org/W3035524453","https://openalex.org/W3048804154","https://openalex.org/W3109228974","https://openalex.org/W3180629550","https://openalex.org/W3185674647","https://openalex.org/W3196221233","https://openalex.org/W3206996142","https://openalex.org/W3214233127","https://openalex.org/W4213210732","https://openalex.org/W4214612132","https://openalex.org/W4225832925","https://openalex.org/W4295682580","https://openalex.org/W4306179773","https://openalex.org/W4306250089","https://openalex.org/W4312258136","https://openalex.org/W4312829261","https://openalex.org/W4315630215","https://openalex.org/W4319299976","https://openalex.org/W4322627157","https://openalex.org/W4376167392","https://openalex.org/W4385248623","https://openalex.org/W4392520333","https://openalex.org/W4398757477","https://openalex.org/W4399728346","https://openalex.org/W4401508046","https://openalex.org/W4404034391","https://openalex.org/W4404034597","https://openalex.org/W4404034620","https://openalex.org/W4404370898","https://openalex.org/W4406356097","https://openalex.org/W4411794411","https://openalex.org/W4412171649","https://openalex.org/W4414760883","https://openalex.org/W4416078079","https://openalex.org/W4417530737","https://openalex.org/W7133204984"],"related_works":[],"abstract_inverted_index":{"The":[0],"prevailing":[1],"single-source":[2],"paradigm":[3],"in":[4],"wireless":[5],"sensing":[6],"produces":[7],"specialized":[8],"models":[9,84,124],"that":[10,59,81,117,122],"are":[11,119],"unscalable":[12],"and":[13,38,74,90,95,105,121,128],"generalize":[14],"poorly":[15],"to":[16,132],"new":[17],"tasks.":[18],"Multi-source":[19],"pre-training":[20,44,57,77,130],"offers":[21],"a":[22,25,42,53,61,75,110],"path":[23],"toward":[24],"generalist":[26],"backbone":[27,63],"but":[28],"poses":[29],"challenges":[30],"including":[31],"task":[32],"heterogeneity,":[33],"data":[34,101],"redundancy,":[35],"structural":[36],"incompatibility,":[37],"the":[39],"lack":[40],"of":[41,113],"general-purpose":[43,62],"objective.":[45,78],"To":[46],"address":[47],"these":[48],"issues,":[49],"we":[50],"propose":[51],"WiSwiss,":[52],"comprehensive":[54],"self-supervised":[55],"multi-source":[56],"framework":[58],"learns":[60],"for":[64,71],"each":[65],"modality.":[66],"WiSwiss":[67,82],"integrates":[68],"semantic":[69],"deduplication":[70],"dataset":[72],"curation":[73],"transformation-invariant":[76],"Experiments":[79],"show":[80],"outperforms":[83],"trained":[85],"from":[86],"scratch,":[87],"improving":[88],"WiFi":[89],"mmWave":[91],"performance":[92],"by":[93,103],"4.5%":[94],"10.3%,":[96],"respectively,":[97],"while":[98],"reducing":[99],"fine-tuning":[100],"requirements":[102],"22.2%":[104],"28.6%.":[106],"We":[107],"also":[108],"present":[109],"qualitative":[111],"study":[112],"scaling":[114],"laws,":[115],"showing":[116],"gains":[118],"task-dependent":[120],"larger":[123],"require":[125],"sufficiently":[126],"large":[127],"diverse":[129],"corpora":[131],"achieve":[133],"substantial":[134],"improvements.":[135]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-09T00:00:00"}