{"id":"https://openalex.org/W7128790372","doi":"https://doi.org/10.1109/jiot.2026.3664751","title":"Can LLMs Be Effective Sensor Processing Copilots?","display_name":"Can LLMs Be Effective Sensor Processing Copilots?","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7128790372","doi":"https://doi.org/10.1109/jiot.2026.3664751"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2026.3664751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3664751","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5048469097","display_name":"Pengrui Quan","orcid":"https://orcid.org/0000-0002-0458-3966"},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pengrui Quan","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-0458-3966","affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123977720","display_name":"Xiaomin Ouyang","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"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaomin Ouyang","raw_affiliation_strings":["The Hong Kong University of Science and Technology, Kowloon, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065874576","display_name":"Jeya Vikranth Jeyakumar","orcid":"https://orcid.org/0000-0003-0322-7086"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeya Vikranth Jeyakumar","raw_affiliation_strings":["Nvidia, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nvidia, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125945190","display_name":"Ziqi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087596","display_name":"Qualcomm (United States)","ror":"https://ror.org/002zrf773","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087596"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziqi Wang","raw_affiliation_strings":["Qualcomm, San Diego, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qualcomm, San Diego, CA, USA","institution_ids":["https://openalex.org/I4210087596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123917598","display_name":"Yang Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Xing","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125895664","display_name":"Mani Srivastava","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mani Srivastava","raw_affiliation_strings":["UCLA, Los Angles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3782-9192","affiliations":[{"raw_affiliation_string":"UCLA, Los Angles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5048469097"],"corresponding_institution_ids":["https://openalex.org/I2799798094"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2682438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"9","first_page":"18513","last_page":"18526"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.13269999623298645,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.13269999623298645,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.09059999883174896,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.06440000236034393,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/benchmark","display_name":"Benchmark (surveying)","score":0.710099995136261},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5497999787330627},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4007999897003174},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.382099986076355},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.35420000553131104},{"id":"https://openalex.org/keywords/risk-management","display_name":"Risk management","score":0.30399999022483826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7570000290870667},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.710099995136261},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5497999787330627},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3490999937057495},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30149999260902405},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C3019252630","wikidata":"https://www.wikidata.org/wiki/Q6549547","display_name":"Limited resources","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.26249998807907104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2614000141620636}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jiot.2026.3664751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3664751","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-169811","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-169811","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2944494573","display_name":null,"funder_award_id":"W911NF1720196","funder_id":"https://openalex.org/F4320338456","funder_display_name":"DEVCOM Army Research Laboratory"},{"id":"https://openalex.org/G3023313847","display_name":null,"funder_award_id":"P41EB028242","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3411093368","display_name":null,"funder_award_id":"FA95502210193","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G5648948408","display_name":null,"funder_award_id":"CNS-2325956","funder_id":"https://openalex.org/F4320319363","funder_display_name":"National Scleroderma Foundation"},{"id":"https://openalex.org/G6310772244","display_name":null,"funder_award_id":"2169310","funder_id":"https://openalex.org/F4320338291","funder_display_name":"Sandia National Laboratories"}],"funders":[{"id":"https://openalex.org/F4320319363","display_name":"National Scleroderma Foundation","ror":null},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"},{"id":"https://openalex.org/F4320338291","display_name":"Sandia National Laboratories","ror":"https://ror.org/01apwpt12"},{"id":"https://openalex.org/F4320338456","display_name":"DEVCOM Army Research Laboratory","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Effective":[0],"sensor":[1,29,48],"data":[2],"processing":[3],"is":[4],"critical":[5],"for":[6,28,42,56,92,208],"cyber-physical":[7],"and":[8,50,68,84,96,108,127,152,210],"IoT":[9],"systems":[10],"but":[11,116],"often":[12],"requires":[13],"specialized":[14],"expertise.":[15],"While":[16],"Large":[17,80],"Language":[18],"Models":[19,82],"(LLMs)":[20],"show":[21,163],"promise":[22],"as":[23,192],"autonomous":[24],"<italic":[25],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[26,216],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">copilots</i>":[27],"processing,":[30],"their":[31],"capabilities":[32],"remain":[33],"underexplored.":[34],"We":[35,72],"introduce":[36],"SensorBench,":[37],"the":[38,145,197],"first":[39],"comprehensive":[40],"benchmark":[41,204],"evaluating":[43,209],"LLMs":[44,58,111,212],"across":[45],"diverse":[46],"real-world":[47],"datasets":[49],"tasks.":[51],"SensorBench":[52],"evaluates":[53],"three":[54],"paradigms":[55],"leveraging":[57],"in":[59,135,174,213],"sensing":[60,214],"tasks:":[61],"Tool-Augmented":[62],"Coding":[63,66],"(TAC),":[64],"Standalone":[65],"(SAC),":[67],"Direct":[69],"Answer":[70],"(DA).":[71],"evaluate":[73],"8":[74],"leading":[75],"LLM":[76],"variants,":[77],"including":[78],"2":[79,85],"Reasoning":[81],"(LRMs)":[83],"domain-specific":[86],"LLMs,":[87],"providing":[88],"a":[89,206],"structured":[90],"reference":[91],"absolute":[93],"performance,":[94,146],"latency,":[95],"resource":[97],"requirements.":[98],"Our":[99,183],"analysis":[100,184],"reveals":[101],"that:":[102],"(1)":[103],"TAC":[104],"significantly":[105],"outperforms":[106],"SAC":[107],"DA;":[109],"(2)":[110],"excel":[112],"at":[113],"simple":[114],"tasks":[115,123],"consistently":[117],"underperform":[118],"domain":[119],"experts":[120],"on":[121],"compositional":[122],"requiring":[124],"parameter":[125],"tuning":[126],"multi-step":[128],"reasoning.":[129],"(3)":[130],"The":[131,161],"reasoning":[132],"mechanism":[133],"introduced":[134],"LRMs":[136],"does":[137],"not":[138],"yield":[139],"substantial":[140],"performance":[141],"gains.":[142,182],"To":[143],"improve":[144],"we":[147],"explore":[148],"four":[149],"prompting":[150,166],"strategies":[151],"fine-tuning":[153,179],"approaches":[154],"(using":[155],"our":[156],"newly":[157],"released":[158],"sensor-processing":[159],"corpus).":[160],"results":[162],"that":[164,186],"self-verification":[165],"proves":[167],"most":[168],"effective,":[169],"outperforming":[170],"other":[171],"methods":[172],"simultaneously":[173],"48%":[175],"of":[176],"tasks,":[177],"while":[178],"yields":[180],"marginal":[181],"suggests":[185],"more":[187],"sophisticated":[188],"interaction":[189],"frameworks,":[190],"such":[191],"signal-level":[193],"self-verification,":[194],"may":[195],"bridge":[196],"gap":[198],"to":[199],"human":[200],"expert-level":[201],"performance.":[202],"This":[203],"provides":[205],"foundation":[207],"improving":[211],"applications<sup":[215],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>.":[217]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-02-14T00:00:00"}
