{"id":"https://openalex.org/W7129562355","doi":"https://doi.org/10.1109/iceconf65644.2025.11379407","title":"Wi-Fi Signal Strength Analyzer Using Deep Learning for Optimized Network Performance in Smart Environments","display_name":"Wi-Fi Signal Strength Analyzer Using Deep Learning for Optimized Network Performance in Smart Environments","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W7129562355","doi":"https://doi.org/10.1109/iceconf65644.2025.11379407"},"language":"en","primary_location":{"id":"doi:10.1109/iceconf65644.2025.11379407","doi":"https://doi.org/10.1109/iceconf65644.2025.11379407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceconf65644.2025.11379407","pdf_url":null,"source":{"id":"https://openalex.org/S7407086746","display_name":"International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5116780607","display_name":"Ashika Deulin J","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277505","display_name":"Jeppiaar Institute of Technology","ror":"https://ror.org/02ygb0883","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405277505"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashika J","raw_affiliation_strings":["Jeppiaar Institute of Technology,Department of ECE,Chennai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology,Department of ECE,Chennai","institution_ids":["https://openalex.org/I4405277505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076327551","display_name":"Niveditha Raj","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277505","display_name":"Jeppiaar Institute of Technology","ror":"https://ror.org/02ygb0883","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405277505"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Niveditha N Raj","raw_affiliation_strings":["Jeppiaar Institute of Technology,Department of ECE,Chennai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology,Department of ECE,Chennai","institution_ids":["https://openalex.org/I4405277505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126189509","display_name":"Anushiya Jasbin A","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277505","display_name":"Jeppiaar Institute of Technology","ror":"https://ror.org/02ygb0883","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405277505"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anushiya Jasbin A","raw_affiliation_strings":["Jeppiaar Institute of Technology,Department of ECE,Chennai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology,Department of ECE,Chennai","institution_ids":["https://openalex.org/I4405277505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126197951","display_name":"Dharanika A","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277505","display_name":"Jeppiaar Institute of Technology","ror":"https://ror.org/02ygb0883","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405277505"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dharanika A","raw_affiliation_strings":["Jeppiaar Institute of Technology,Department of ECE,Chennai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology,Department of ECE,Chennai","institution_ids":["https://openalex.org/I4405277505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091269098","display_name":"T. Sripriya","orcid":"https://orcid.org/0000-0003-0600-1605","observed_orcids":["https://orcid.org/0000-0003-0600-1605"]},"institutions":[{"id":"https://openalex.org/I4405277505","display_name":"Jeppiaar Institute of Technology","ror":"https://ror.org/02ygb0883","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405277505"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"T. Sripriya","raw_affiliation_strings":["Jeppiaar Institute of Technology,Department of ECE,Chennai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology,Department of ECE,Chennai","institution_ids":["https://openalex.org/I4405277505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4405277505"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.798799991607666,"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":0.798799991607666,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.16380000114440918,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.010599999688565731,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/received-signal-strength-indicator","display_name":"received signal strength indicator","score":0.6240000128746033},{"id":"https://openalex.org/keywords/channel-state-information","display_name":"channel state information","score":0.953000009059906},{"id":"https://openalex.org/keywords/crowdsensing","display_name":"crowdsensing","score":0.8619999885559082},{"id":"https://openalex.org/keywords/physics-informed-neural-networks","display_name":"physics-informed neural networks","score":0.7229999899864197},{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.6809999942779541},{"id":"https://openalex.org/keywords/uncertainty-estimation","display_name":"uncertainty estimation","score":0.609000027179718},{"id":"https://openalex.org/keywords/counterfactual-prediction","display_name":"counterfactual prediction","score":0.5740000009536743},{"id":"https://openalex.org/keywords/structural-causal-models","display_name":"structural causal models","score":0.7559999823570251},{"id":"https://openalex.org/keywords/federated-learning","display_name":"federated learning","score":0.753000020980835},{"id":"https://openalex.org/keywords/multi-agent-control","display_name":"multi-agent control","score":0.7360000014305115},{"id":"https://openalex.org/keywords/edge-computing","display_name":"edge computing","score":0.746999979019165},{"id":"https://openalex.org/keywords/smart-environments","display_name":"smart environments","score":0.6650000214576721},{"id":"https://openalex.org/keywords/network-performance-optimization","display_name":"network performance optimization","score":0.699999988079071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657800018787384},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6216999888420105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5855000019073486},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4345000088214874},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27799999713897705},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.25110000371932983},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceconf65644.2025.11379407","doi":"https://doi.org/10.1109/iceconf65644.2025.11379407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceconf65644.2025.11379407","pdf_url":null,"source":{"id":"https://openalex.org/S7407086746","display_name":"International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W4391688040","https://openalex.org/W4396930521","https://openalex.org/W4401453688","https://openalex.org/W4402569989","https://openalex.org/W4403060225","https://openalex.org/W4404280027","https://openalex.org/W4404688291","https://openalex.org/W4407098369","https://openalex.org/W4409129684","https://openalex.org/W4410859616","https://openalex.org/W4411019828"],"related_works":["https://openalex.org/W4213358579","https://openalex.org/W3198844990","https://openalex.org/W7129011438","https://openalex.org/W1995145742","https://openalex.org/W7171438403","https://openalex.org/W7171718270","https://openalex.org/W4285274359","https://openalex.org/W3179016645","https://openalex.org/W4323521031","https://openalex.org/W6903964173"],"abstract_inverted_index":{"This":[0],"work":[1],"extended":[2],"a":[3,20,39,64,76,98,111,120,166,171,177,187,194],"Wi-Fi":[4],"Signal":[5],"Strength":[6],"Analyzer":[7],"designed":[8],"to":[9,46,63,156,222],"operate":[10],"in":[11,180],"smart":[12],"environments":[13],"and":[14,29,35,72,75,93,96,123,134,176,213,219],"combined":[15],"crowdsourced,":[16],"RSSI/CSI-based":[17],"sensing":[18],"with":[19,50,129,160,186,210],"physics-enhanced,":[21],"floormap-aware":[22],"deep":[23],"path":[24],"through":[25],"dense":[26],"radio-map":[27],"recovery":[28],"closed-loop":[30],"tuning.":[31],"To":[32],"learn":[33],"site":[34],"hardware":[36],"agnostic":[37],"embeddings,":[38],"cross-modal":[40],"self-supervised":[41],"pretraining":[42],"strategy":[43],"was":[44,142,154],"used":[45,60],"align":[47],"masked":[48],"CSI":[49],"co-temporal":[51],"IMU":[52],"traces.":[53],"Graph":[54],"embeddings":[55],"derived":[56],"using":[57],"BIM":[58],"were":[59],"as":[61,143,145],"conditioning":[62],"multipath-aware":[65],"physics-informed":[66],"neural":[67],"field":[68],"which":[69],"imposed":[70],"propagation":[71],"boundary":[73],"constraints,":[74],"conditional":[77],"diffusion":[78],"model":[79],"generated":[80],"uncertainty-aware":[81],"densified":[82],"maps.":[83],"A":[84],"structural":[85],"causal":[86,214],"layer":[87],"estimated":[88],"counterfactuals":[89],"of":[90,114,183,191,206],"channel,":[91],"power,":[92],"beam":[94],"variations":[95],"directed":[97],"federated":[99],"multi-agent":[100],"controller":[101],"at":[102,146],"edge":[103],"accelerators.":[104],"Across":[105],"ten":[106],"buildings,":[107],"the":[108,181,184,204],"system":[109],"achieved":[110],"reconstruction":[112],"accuracy":[113,141],"RMSE":[115],"=":[116,131],"2.9":[117],"dBm":[118,125,148],"on":[119,126,193],"held-out":[121],"campus":[122],"3.1-3.6":[124],"unseen":[127],"sites,":[128],"PSNR":[130],"27.6":[132],"dB":[133],"well-calibrated":[135],"uncertainty":[136],"(ECE":[137],"\u2248":[138],"3.0%).":[139],"Hit-rate":[140],"high":[144],"\u00b13":[147],"(94.1":[149],"%);":[150],"counterfactual":[151],"effect":[152],"prediction":[153],"able":[155],"predict":[157],"actualized":[158],"outcomes":[159],"92.3%":[161],"accuracy.":[162],"Online":[163],"control":[164],"showed":[165],"24.8%":[167],"median":[168],"throughput":[169],"increase,":[170],"31.0%":[172],"p95":[173,188],"latency":[174,190],"decrease":[175],"10.7%":[178],"reduction":[179],"energy":[182],"AP,":[185],"inference":[189],"148165ms":[192],"Raspberry":[195],"Pi":[196],"5":[197],"+":[198],"Edge-TPU.":[199],"These":[200],"findings":[201],"revealed":[202],"that":[203],"combination":[205],"data":[207],"driven":[208],"learning":[209],"physicalized":[211],"priors":[212],"thinking":[215],"provided":[216],"correct":[217],"maps":[218],"actionable":[220],"interventions":[221],"achieve":[223],"consistent":[224],"network":[225],"performance":[226],"improvements.":[227]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-18T00:00:00"}