Machine Learning for Advanced Wireless Sensor Networks: A Review

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dc.contributor.authorKim, Taeyoungko
dc.contributor.authorVecchietti, Luiz Felipeko
dc.contributor.authorChoi, Kyujinko
dc.contributor.authorLee, Sangkeumko
dc.contributor.authorHar, Dongsooko
dc.date.accessioned2021-06-21T05:30:09Z-
dc.date.available2021-06-21T05:30:09Z-
dc.date.created2020-12-03-
dc.date.created2020-12-03-
dc.date.issued2021-06-
dc.identifier.citationIEEE SENSORS JOURNAL, v.21, no.11, pp.12379 - 12397-
dc.identifier.issn1530-437X-
dc.identifier.urihttp://hdl.handle.net/10203/286005-
dc.description.abstractWireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures. IEEE-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleMachine Learning for Advanced Wireless Sensor Networks: A Review-
dc.typeArticle-
dc.identifier.wosid000655846200008-
dc.identifier.scopusid2-s2.0-85098752175-
dc.type.rimsART-
dc.citation.volume21-
dc.citation.issue11-
dc.citation.beginningpage12379-
dc.citation.endingpage12397-
dc.citation.publicationnameIEEE SENSORS JOURNAL-
dc.identifier.doi10.1109/JSEN.2020.3035846-
dc.contributor.localauthorHar, Dongsoo-
dc.contributor.nonIdAuthorChoi, Kyujin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorWireless sensor networks-
dc.subject.keywordAuthorSensors-
dc.subject.keywordAuthorSupervised learning-
dc.subject.keywordAuthorUnsupervised learning-
dc.subject.keywordAuthorSupport vector machines-
dc.subject.keywordAuthorNeural networks-
dc.subject.keywordAuthorSecurity-
dc.subject.keywordAuthorWireless sensor networks-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordPlusENERGY-EFFICIENT-
dc.subject.keywordPlusCONGESTION CONTROL-
dc.subject.keywordPlusCLOCK SYNCHRONIZATION-
dc.subject.keywordPlusROUTING ALGORITHM-
dc.subject.keywordPlusRELAY SELECTION-
dc.subject.keywordPlusCOOPERATIVE COMMUNICATIONS-
dc.subject.keywordPlusTIME SYNCHRONIZATION-
dc.subject.keywordPlusEVENT DETECTION-
dc.subject.keywordPlusLINK QUALITY-
dc.subject.keywordPlusMISSING DATA-
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