{"id":"https://openalex.org/W7165632155","doi":"https://doi.org/10.48550/arxiv.2606.22496","title":"Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing","display_name":"Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing","publication_year":2026,"publication_date":"2026-06-21","ids":{"openalex":"https://openalex.org/W7165632155","doi":"https://doi.org/10.48550/arxiv.2606.22496"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22496","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22496","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.22496","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115561201","display_name":"Ayg\u00fcn Varol","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varol, Ayg\u00fcn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139159465","display_name":"Katarzyna Ko\u0142odziej","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ko\u0142odziej, Katarzyna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086418899","display_name":"\u0141ukasz Sobczak","orcid":"https://orcid.org/0000-0001-9439-1812"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sobczak, \u0141ukasz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068582854","display_name":"Micha\u0142 Romaszewski","orcid":"https://orcid.org/0000-0002-8227-929X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Romaszewski, Micha\u0142","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061607408","display_name":"Przemys\u0142aw G\u0142omb","orcid":"https://orcid.org/0000-0002-0215-4674"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u0142omb, Przemys\u0142aw","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010367405","display_name":"Naser Hossein Motlagh","orcid":"https://orcid.org/0000-0001-9923-9879"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Motlagh, Naser Hossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060251671","display_name":"Mirka Leino","orcid":"https://orcid.org/0000-0002-0465-4197"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leino, Mirka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010794816","display_name":"Johanna Virkki","orcid":"https://orcid.org/0000-0002-2216-7296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Virkki, Johanna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.49639999866485596,"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"}},"topics":[{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.49639999866485596,"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"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.33550000190734863,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.014999999664723873,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6277999877929688},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6269000172615051},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.6252999901771545},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.567300021648407},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.44929999113082886},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4275999963283539},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.3840000033378601},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.695900022983551},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6277999877929688},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6269000172615051},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.567300021648407},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4194999933242798},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35510000586509705},{"id":"https://openalex.org/C2777103469","wikidata":"https://www.wikidata.org/wiki/Q1231558","display_name":"Smart city","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.2969000041484833},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2776356880","wikidata":"https://www.wikidata.org/wiki/Q5381206","display_name":"Environmental data","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22496","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22496","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.22496","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22496","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.45723065733909607,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,37],"(LLMs)":[3],"offer":[4],"a":[5,69,119],"natural-language":[6],"interface":[7],"for":[8,64],"interpreting":[9],"Internet":[10],"of":[11,61],"Things":[12],"(IoT)":[13],"sensor":[14,45,87],"data":[15],"in":[16,226],"smart":[17,227],"environments;":[18],"however,":[19],"cloud":[20,139],"deployment":[21],"introduces":[22],"latency,":[23],"privacy,":[24],"and":[25,78,91,108,115,129,133,137,145,150,174,221],"connectivity":[26],"concerns.":[27],"Local":[28,188],"LLMs":[29,63,140],"can":[30,56,215],"reduce":[31],"these":[32],"limitations,":[33],"but":[34],"compact":[35,92],"edge-deployable":[36],"often":[38],"show":[39,155],"weaker":[40],"numerical":[41],"reasoning":[42],"when":[43],"raw":[44,76,86,182],"readings":[46],"are":[47],"provided":[48],"directly.":[49],"This":[50],"paper":[51],"investigates":[52],"whether":[53],"prompt-side":[54,213],"preprocessing":[55,214],"improve":[57],"the":[58,181,184,192,217],"accuracy-latency":[59],"trade-off":[60],"local":[62,136,166],"environmental":[65,93,131],"monitoring.":[66],"We":[67,117],"propose":[68],"structured":[70],"prompt":[71,143,157],"construction":[72],"framework":[73],"that":[74,156,211],"transforms":[75],"air-quality":[77,110],"thermal-comfort":[79],"measurements":[80],"into":[81],"progressively":[82],"enriched":[83,186],"textual":[84],"representations:":[85],"values,":[88],"threshold-aware":[89],"descriptions,":[90],"summary":[94],"flags.":[95],"The":[96],"approach":[97],"is":[98,191],"evaluated":[99],"using":[100],"indoor":[101],"Raspberry":[102],"Pi/BME680":[103],"datasets":[104,111],"from":[105,112,169,175,180],"Tampere":[106],"University":[107],"outdoor":[109],"Helsinki,":[113],"Katowice,":[114],"Warsaw.":[116],"construct":[118],"binary":[120],"LLM":[121],"query":[122],"dataset":[123],"covering":[124],"air":[125],"quality,":[126],"thermal":[127],"comfort,":[128],"joint":[130],"conditions,":[132],"evaluate":[134],"five":[135,138],"across":[141],"three":[142],"variants":[144],"two":[146],"inference":[147,190,206],"modes,":[148],"with":[149,195],"without":[151],"chain-of-thought":[152],"prompting.":[153],"Results":[154],"enrichment":[158],"substantially":[159,204],"improves":[160],"local-model":[161],"accuracy.":[162],"In":[163],"No-CoT":[164,189],"mode,":[165],"accuracy":[167],"increases":[168,205],"50.9%":[170],"to":[171,177,183,199],"81.7%":[172],"indoors":[173],"63.7%":[176],"89.3%":[178],"outdoors":[179],"most":[185],"prompt.":[187],"fastest":[193],"configuration,":[194],"mean":[196],"latency":[197],"close":[198],"0.22":[200],"s,":[201],"while":[202],"CoT":[203],"time.":[207],"These":[208],"findings":[209],"suggest":[210],"lightweight":[212],"narrow":[216],"local--cloud":[218],"performance":[219],"gap":[220],"support":[222],"low-latency":[223],"IoT":[224],"analytics":[225],"environments.":[228]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}