{"id":"https://openalex.org/W3131021570","doi":"https://doi.org/10.1109/comsnets51098.2021.9352852","title":"Indoor RSSI Prediction using Machine Learning for Wireless Networks","display_name":"Indoor RSSI Prediction using Machine Learning for Wireless Networks","publication_year":2021,"publication_date":"2021-01-05","ids":{"openalex":"https://openalex.org/W3131021570","doi":"https://doi.org/10.1109/comsnets51098.2021.9352852","mag":"3131021570"},"language":"en","primary_location":{"id":"doi:10.1109/comsnets51098.2021.9352852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets51098.2021.9352852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on COMmunication Systems & NETworkS (COMSNETS)","raw_type":"proceedings-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/A5073889458","display_name":"Nibin Raj","orcid":"https://orcid.org/0000-0002-9995-9398"},"institutions":[{"id":"https://openalex.org/I882761360","display_name":"Indian Institute of Space Science and Technology","ror":"https://ror.org/05k37ht14","country_code":"IN","type":"education","lineage":["https://openalex.org/I882761360"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nibin Raj","raw_affiliation_strings":["IIST, Thiruvananthapuram"],"affiliations":[{"raw_affiliation_string":"IIST, Thiruvananthapuram","institution_ids":["https://openalex.org/I882761360"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5073889458"],"corresponding_institution_ids":["https://openalex.org/I882761360"],"apc_list":null,"apc_paid":null,"fwci":1.7046,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.83829883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"372","last_page":"374"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998999834060669,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998999834060669,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9994999766349792,"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/T12146","display_name":"Power Line Communications and Noise","score":0.9984999895095825,"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/computer-science","display_name":"Computer science","score":0.7327005863189697},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7039194703102112},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6710833311080933},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.5676131844520569},{"id":"https://openalex.org/keywords/signal-strength","display_name":"Signal strength","score":0.5670672655105591},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5273338556289673},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.520188570022583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4687139093875885},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4467334747314453},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4324359893798828},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.4280818700790405},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.41043686866760254},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35454654693603516},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2835240364074707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1359427273273468},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08037379384040833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7327005863189697},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7039194703102112},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6710833311080933},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.5676131844520569},{"id":"https://openalex.org/C176808163","wikidata":"https://www.wikidata.org/wiki/Q17105794","display_name":"Signal strength","level":3,"score":0.5670672655105591},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5273338556289673},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.520188570022583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4687139093875885},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4467334747314453},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4324359893798828},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4280818700790405},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.41043686866760254},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35454654693603516},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2835240364074707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1359427273273468},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08037379384040833},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comsnets51098.2021.9352852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets51098.2021.9352852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on COMmunication Systems & NETworkS (COMSNETS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W595252221","https://openalex.org/W2096032932","https://openalex.org/W2099878889","https://openalex.org/W2145417574","https://openalex.org/W2616222121","https://openalex.org/W2917609517","https://openalex.org/W2978631110","https://openalex.org/W2986489130"],"related_works":["https://openalex.org/W2811187992","https://openalex.org/W803509314","https://openalex.org/W2132491819","https://openalex.org/W3042921537","https://openalex.org/W4360615906","https://openalex.org/W2359189099","https://openalex.org/W335743615","https://openalex.org/W2075959792","https://openalex.org/W4225911062","https://openalex.org/W2388767763"],"abstract_inverted_index":{"We":[0,102,118],"consider":[1],"the":[2,57,91,98,120,145],"study":[3],"of":[4,20,93,100,126,152],"received":[5],"signal":[6],"strength":[7],"indication":[8],"(RSSI)":[9],"prediction":[10,26,89,99,122],"in":[11,27,33,124],"an":[12,75],"indoor":[13],"room":[14,58],"environment":[15,30,155],"using":[16],"a":[17,28,34,137],"small":[18],"set":[19],"actual":[21],"measurement":[22],"data.":[23],"The":[24,148],"RSSI":[25,88,121],"test":[29,146,154],"is":[31,156],"important":[32],"network":[35,78],"planning":[36],"strategy.":[37],"Traditional":[38],"models":[39],"are":[40,49,115],"based":[41],"on":[42,52],"either":[43],"empirical":[44],"or":[45,80],"deterministic":[46],"models,":[47],"which":[48],"time-consuming":[50],"depending":[51],"many":[53,62],"factors":[54],"such":[55],"as":[56,158],"structure,":[59],"obstacles,":[60],"and":[61,90,131],"more.":[63],"In":[64],"this":[65,87],"paper,":[66],"we":[67],"investigate":[68],"any":[69],"simple":[70],"machine":[71],"learning":[72],"model":[73,83],"like":[74,107],"artificial":[76],"neural":[77],"(ANN)":[79],"linear":[81],"regression":[82],"that":[84,96,104],"can":[85],"do":[86],"estimation":[92],"environmental-related":[94],"parameters":[95,106],"affect":[97],"RSSI.":[101],"assume":[103],"some":[105],"transmit":[108],"power,":[109],"antenna":[110],"height,":[111],"wall":[112],"material":[113],"properties":[114],"kept":[116],"fixed.":[117],"illustrate":[119],"performance":[123],"terms":[125],"mean":[127,132],"squared":[128],"error":[129,134],"(MSE)":[130],"absolute":[133],"(MAE)":[135],"for":[136],"dataset":[138],"with":[139],"1030":[140],"data":[141],"points":[142],"collected":[143],"from":[144],"environment.":[147],"path":[149],"loss":[150],"exponent":[151],"our":[153],"estimated":[157],"1.97.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-02-24T08:05:00.420983","created_date":"2025-10-10T00:00:00"}