{"id":"https://openalex.org/W2887731362","doi":"https://doi.org/10.1109/icc.2018.8422402","title":"Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT","display_name":"Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2887731362","doi":"https://doi.org/10.1109/icc.2018.8422402","mag":"2887731362"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2018.8422402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1812.04872","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tie Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tie Luo","raw_affiliation_strings":["Institute for Infocomm Research, A*STAR, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*STAR, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sai G. Nagarajan","orcid":null},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Sai G. Nagarajan","raw_affiliation_strings":["Singapore University of Technology and Design, Singapore, SG"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design, Singapore, SG","institution_ids":["https://openalex.org/I152815399"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.4752,"has_fulltext":false,"cited_by_count":160,"citation_normalized_percentile":{"value":0.9848262,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9934999942779541,"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/T11220","display_name":"Water Systems and Optimization","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/autoencoder","display_name":"Autoencoder","score":0.8909000158309937},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8075000047683716},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.7213000059127808},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5602999925613403},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.5184999704360962},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5094000101089478},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4977000057697296},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.44179999828338623},{"id":"https://openalex.org/keywords/alarm","display_name":"ALARM","score":0.4350000023841858}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8909000158309937},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8075000047683716},{"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/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.7213000059127808},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5602999925613403},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5094000101089478},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4440999925136566},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.44179999828338623},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39570000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34360000491142273},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33880001306533813},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2727999985218048},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icc.2018.8422402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1812.04872","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.04872","pdf_url":"https://arxiv.org/pdf/1812.04872","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1812.04872","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.04872","pdf_url":"https://arxiv.org/pdf/1812.04872","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1989742024","https://openalex.org/W2023332207","https://openalex.org/W2037660732","https://openalex.org/W2082384736","https://openalex.org/W2107106521","https://openalex.org/W2111184007","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2143407377","https://openalex.org/W2156209126","https://openalex.org/W2165116724","https://openalex.org/W6679329134","https://openalex.org/W6681096077"],"related_works":[],"abstract_inverted_index":{"Wireless":[0],"sensor":[1,201],"networks":[2,98],"(WSN)":[3],"are":[4],"fundamental":[5],"to":[6,101,170,232,234,244],"the":[7,14,17,20,56,62,65,81,91,103,117,136,145,149,159,171,175,245],"Internet":[8],"of":[9,40,55,59,64,83,249],"Things":[10],"(IoT)":[11],"by":[12,158,190],"bridging":[13],"gap":[15],"between":[16],"physical":[18],"and":[19,46,61,79,116,147,187,200,223,236],"cyber":[21],"worlds.":[22],"Anomaly":[23],"detection":[24,105,217,221],"is":[25,34,52,76,180,229],"a":[26,69,109,131,162,195,240],"critical":[27],"task":[28,51,154],"in":[29,130,239],"this":[30,50,74,88],"context":[31],"as":[32,43],"it":[33],"responsible":[35],"for":[36,90,138],"identifying":[37],"various":[38],"events":[39],"interests":[41],"such":[42,121],"equipment":[44],"faults":[45],"undiscovered":[47],"phenomena.":[48],"However,":[49],"challenging":[53],"because":[54],"elusive":[57],"nature":[58],"anomalies":[60,124],"volatility":[63],"ambient":[66],"environments.":[67],"In":[68,87,168],"resource-scarce":[70],"setting":[71],"like":[72],"WSN,":[73],"challenge":[75],"further":[77],"elevated":[78],"weakens":[80],"suitability":[82],"many":[84],"existing":[85],"solutions.":[86],"paper,":[89],"first":[92],"time,":[93],"we":[94,208],"introduce":[95],"autoencoder":[96,252],"neural":[97,253],"into":[99],"WSN":[100,197],"solve":[102],"anomaly":[104,216],"problem.":[106],"We":[107],"design":[108],"two-part":[110],"algorithm":[111],"that":[112,122,212],"resides":[113],"on":[114,178],"sensors":[115,129,143,179],"IoT":[118],"cloud":[119,160],"respectively,":[120],"(i)":[123],"can":[125,155],"be":[126,156],"detected":[127],"at":[128],"fully":[132],"distributed":[133],"manner":[134],"without":[135],"need":[137],"communicating":[139],"with":[140,161],"any":[141],"other":[142],"or":[144],"cloud,":[146],"(ii)":[148],"relatively":[150],"more":[151],"computation-intensive":[152],"learning":[153,247],"handled":[157],"much":[163],"lower":[164],"(and":[165],"configurable)":[166],"frequency.":[167],"addition":[169],"minimal":[172],"communication":[173],"overhead,":[174],"computational":[176],"load":[177],"also":[181,230],"very":[182],"low":[183,224],"(of":[184],"polynomial":[185],"complexity)":[186],"readily":[188],"affordable":[189],"most":[191],"COTS":[192],"sensors.":[193],"Using":[194],"real":[196],"indoor":[198],"testbed":[199],"data":[202],"collected":[203],"over":[204],"4":[205],"consecutive":[206],"months,":[207],"demonstrate":[209],"via":[210],"experiments":[211],"our":[213,250],"proposed":[214],"autoencoder-based":[215],"mechanism":[218],"achieves":[219],"high":[220],"accuracy":[222],"false":[225],"alarm":[226],"rate.":[227],"It":[228],"able":[231],"adapt":[233],"unforeseeable":[235],"new":[237],"changes":[238],"non-stationary":[241],"environment,":[242],"thanks":[243],"unsupervised":[246],"feature":[248],"chosen":[251],"networks.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":34},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2018-08-22T00:00:00"}
