{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T06:04:24Z","timestamp":1769234664231,"version":"3.49.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2008085MF203"],"award-info":[{"award-number":["2008085MF203"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Foundation of State Key Laboratory of Networking and Switching Technology","award":["No. SKLNST-2018-1-10"],"award-info":[{"award-number":["No. SKLNST-2018-1-10"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61402013, No. 31671589"],"award-info":[{"award-number":["No. 61402013, No. 31671589"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:p> This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called \u201cdeep belief network online quarter-sphere support vector machine,\u201d which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%\u20135%. <\/jats:p>","DOI":"10.1177\/1550147720963835","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T07:28:34Z","timestamp":1602055714000},"page":"155014772096383","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Fast outlier detection for high-dimensional data of wireless sensor networks"],"prefix":"10.1177","volume":"16","author":[{"given":"Yan","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Information and Computer, Anhui Agricultural University, Hefei, China"},{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9411-5444","authenticated-orcid":false,"given":"Xinhong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Information and Computer, Anhui Agricultural University, Hefei, China"}]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information and Computer, Anhui Agricultural University, Hefei, China"}]},{"given":"Wu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Computer, Anhui Agricultural University, Hefei, China"}]}],"member":"179","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"bibr1-1550147720963835","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2013.01.010"},{"key":"bibr2-1550147720963835","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9370-y"},{"key":"bibr3-1550147720963835","volume-title":"Observing the unobservable: distributed online outlier detection in wireless sensor 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