{"id":"https://openalex.org/W4387385693","doi":"https://doi.org/10.1109/access.2023.3322370","title":"Anomaly Detection in Coastal Wireless Sensors via Efficient Deep Sequential Learning","display_name":"Anomaly Detection in Coastal Wireless Sensors via Efficient Deep Sequential Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387385693","doi":"https://doi.org/10.1109/access.2023.3322370"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3322370","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322370","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2023.3322370","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062402085","display_name":"Mustafa Matar","orcid":"https://orcid.org/0000-0003-2639-5808"},"institutions":[{"id":"https://openalex.org/I111236770","display_name":"University of Vermont","ror":"https://ror.org/0155zta11","country_code":"US","type":"education","lineage":["https://openalex.org/I111236770"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mustafa Matar","raw_affiliation_strings":["Department of Electrical Engineering, The University of Vermont, Burlington, VT, USA"],"raw_orcid":"https://orcid.org/0000-0003-2639-5808","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, The University of Vermont, Burlington, VT, USA","institution_ids":["https://openalex.org/I111236770"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048747574","display_name":"Tian Xia","orcid":"https://orcid.org/0000-0002-4395-7350"},"institutions":[{"id":"https://openalex.org/I7947594","display_name":"University of Maine","ror":"https://ror.org/01adr0w49","country_code":"US","type":"education","lineage":["https://openalex.org/I2802397601","https://openalex.org/I7947594"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Xia","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Maine, Orono, ME, USA"],"raw_orcid":"https://orcid.org/0000-0002-4395-7350","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Maine, Orono, ME, USA","institution_ids":["https://openalex.org/I7947594"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076092333","display_name":"Kimberly Huguenard","orcid":"https://orcid.org/0000-0001-6285-3082"},"institutions":[{"id":"https://openalex.org/I7947594","display_name":"University of Maine","ror":"https://ror.org/01adr0w49","country_code":"US","type":"education","lineage":["https://openalex.org/I2802397601","https://openalex.org/I7947594"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kimberly Huguenard","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Maine, Orono, ME, USA"],"raw_orcid":"https://orcid.org/0000-0001-6285-3082","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Maine, Orono, ME, USA","institution_ids":["https://openalex.org/I7947594"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040093787","display_name":"Dryver R. Huston","orcid":"https://orcid.org/0000-0002-0957-0574"},"institutions":[{"id":"https://openalex.org/I111236770","display_name":"University of Vermont","ror":"https://ror.org/0155zta11","country_code":"US","type":"education","lineage":["https://openalex.org/I111236770"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dryver Huston","raw_affiliation_strings":["Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA"],"raw_orcid":"https://orcid.org/0000-0002-0957-0574","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA","institution_ids":["https://openalex.org/I111236770"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001816279","display_name":"Safwan Wshah","orcid":"https://orcid.org/0000-0001-5051-7719"},"institutions":[{"id":"https://openalex.org/I111236770","display_name":"University of Vermont","ror":"https://ror.org/0155zta11","country_code":"US","type":"education","lineage":["https://openalex.org/I111236770"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Safwan Wshah","raw_affiliation_strings":["Department of Computer Science, The University of Vermont, Burlington, VT, USA"],"raw_orcid":"https://orcid.org/0000-0001-5051-7719","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Vermont, Burlington, VT, USA","institution_ids":["https://openalex.org/I111236770"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.7802,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88048823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"110260","last_page":"110271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9958000183105469,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.8146741390228271},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.7288258075714111},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7250005006790161},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.6502737998962402},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6166082620620728},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5435547232627869},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4939895272254944},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4121502637863159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3583028316497803},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3249567151069641},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15643924474716187},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09517547488212585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8146741390228271},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.7288258075714111},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7250005006790161},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.6502737998962402},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6166082620620728},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5435547232627869},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4939895272254944},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4121502637863159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3583028316497803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3249567151069641},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15643924474716187},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09517547488212585},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3322370","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322370","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e85507b158ff41c6b020389315b0c536","is_oa":true,"landing_page_url":"https://doaj.org/article/e85507b158ff41c6b020389315b0c536","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 110260-110271 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3322370","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322370","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8899999856948853,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G7979286095","display_name":"RII Track-2 FEC: Advancing Research Towards Industries of the Future to Ensure Economic Growth for EPSCoR Jurisdictions - Advanced Wireless - Integration with Infrastructure System","funder_award_id":"2119485","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W585709381","https://openalex.org/W1522301498","https://openalex.org/W1588594383","https://openalex.org/W1964369900","https://openalex.org/W1970510882","https://openalex.org/W1977295820","https://openalex.org/W1985768685","https://openalex.org/W2010921848","https://openalex.org/W2026381796","https://openalex.org/W2034548879","https://openalex.org/W2037334141","https://openalex.org/W2059728306","https://openalex.org/W2064675550","https://openalex.org/W2072566913","https://openalex.org/W2088196391","https://openalex.org/W2097998348","https://openalex.org/W2100766019","https://openalex.org/W2101087742","https://openalex.org/W2110811672","https://openalex.org/W2119144962","https://openalex.org/W2122646361","https://openalex.org/W2127949150","https://openalex.org/W2134645598","https://openalex.org/W2135053460","https://openalex.org/W2148588185","https://openalex.org/W2151113359","https://openalex.org/W2152282834","https://openalex.org/W2159872370","https://openalex.org/W2160450532","https://openalex.org/W2163336863","https://openalex.org/W2168452204","https://openalex.org/W2220701491","https://openalex.org/W2518816274","https://openalex.org/W2785362611","https://openalex.org/W2886753213","https://openalex.org/W2900063223","https://openalex.org/W2909960414","https://openalex.org/W2914091078","https://openalex.org/W2944017778","https://openalex.org/W2948517885","https://openalex.org/W2954714931","https://openalex.org/W2960833983","https://openalex.org/W2963078493","https://openalex.org/W2963122961","https://openalex.org/W2964228333","https://openalex.org/W2994702256","https://openalex.org/W2999269122","https://openalex.org/W3060435609","https://openalex.org/W3095146658","https://openalex.org/W3098957257","https://openalex.org/W3105931142","https://openalex.org/W3108835732","https://openalex.org/W3125413782","https://openalex.org/W3184127157","https://openalex.org/W4213138287","https://openalex.org/W4221140505","https://openalex.org/W4310072815","https://openalex.org/W4377044057","https://openalex.org/W4383501681","https://openalex.org/W4385245566","https://openalex.org/W4385763767","https://openalex.org/W6631190155","https://openalex.org/W6674385629","https://openalex.org/W6677580257","https://openalex.org/W6680930200","https://openalex.org/W6734062232","https://openalex.org/W6739901393","https://openalex.org/W6811309592"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2105642232","https://openalex.org/W1975451135","https://openalex.org/W3197833032","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W2084724079","https://openalex.org/W4246416108","https://openalex.org/W2111773953"],"abstract_inverted_index":{"Wireless":[0],"Sensor":[1],"Networks":[2],"(WSNs)":[3],"encounter":[4],"a":[5,82,172,183],"substantial":[6],"challenge":[7],"when":[8,89],"it":[9,27],"comes":[10],"to":[11,21,31,61,74,167],"energy":[12,43,87,117],"conservation.":[13],"As":[14],"sensor":[15,127],"nodes":[16],"rely":[17],"on":[18,86],"battery":[19],"power":[20],"operate":[22],"in":[23,59,77,92,196],"unattended":[24],"environments,":[25],"and":[26,106,162,178,194],"can":[28,71,163],"be":[29,72,164],"inconvenient":[30],"recharge":[32],"the":[33,37,49,122,129,136,150,157,188],"batteries.":[34],"So,":[35],"maximizing":[36],"network\u2019s":[38],"operational":[39],"lifetime":[40],"by":[41,100],"minimizing":[42],"consumption":[44],"is":[45,55,176,191],"crucial":[46],"for":[47,95,144,159],"improving":[48],"quality":[50],"of":[51,103,120,125,139],"service.":[52],"Anomaly":[53],"detection":[54,70],"widely":[56],"used":[57],"technique":[58],"WSNs":[60],"identify":[62],"anomalies":[63,91],"or":[64],"unusual":[65],"events.":[66],"However,":[67],"timely":[68],"anomaly":[69],"challenging":[73],"execute":[75],"reliably":[76],"real-time.":[78],"This":[79,114],"study":[80],"presents":[81],"methodology":[83],"that":[84,187],"focuses":[85],"efficiency":[88],"detecting":[90],"real-time":[93],"settings":[94],"multivariate":[96],"time":[97,109,123,137],"series":[98,124,138],"data":[99,105,161,174],"utilizing":[101],"compression":[102],"measurement":[104,112],"reducing":[107],"operating":[108],"without":[110],"compromising":[111],"resolution.":[113],"effectively":[115],"reduces":[116],"consumption.":[118],"Instead":[119],"modeling":[121],"each":[126],"individually,":[128],"proposed":[130,151,189],"deep":[131,152],"sequential":[132,153],"learning":[133,154],"approach":[134,155,190],"models":[135],"multiple":[140],"sensors":[141],"concurrently,":[142],"accounting":[143],"potential":[145],"interactions":[146],"among":[147],"them.":[148],"Additionally,":[149],"eliminates":[156],"need":[158],"labeled":[160],"directly":[165],"applied":[166],"real-world":[168,184],"scenarios":[169],"where":[170],"labeling":[171],"large":[173],"stream":[175],"impractical":[177],"time-consuming.":[179],"Finally,":[180],"experiments":[181],"with":[182],"WSN":[185],"demonstrate":[186],"both":[192],"adequate":[193],"robust":[195],"practice.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}