{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:44:08Z","timestamp":1771955048025,"version":"3.50.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":227,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF067"],"award-info":[{"award-number":["ZR2020MF067"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile and wireless sources. The data to be transmitted is too much compared to link capacity. Labelling data and transmit only useful part of the collected data or their features is a promising solution for this challenge. Abnormal data are valuable due to the need to train models and to detect anomalies when being compared to already overflowing normal data. Labelling can be done in data sources or edges to balance the load and computing between sources, edges, and centres. However, unsupervised labelling method is still a challenge preventing to implement the above solutions. Two main problems in unsupervised labelling are long\u2010term dynamic multiseasonality and heteroscedasticity. This paper proposes a data\u2010driven method to handle modelling and heteroscedasticity problems. The method contains the following main steps. First, raw data are preprocessed and grouped. Second, main models are built for each group. Third, models are adapted back to the original measured data to get raw residuals. Fourth, raw residuals go through deheteroscedasticity and become normalized residuals. Finally, normalized residuals are used to conduct anomaly detection. The experimental results with real\u2010world data show that our method successfully increases receiver\u2010operating characteristic (AUC) by about 30%.<\/jats:p>","DOI":"10.1155\/2021\/6692390","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T22:50:15Z","timestamp":1629154215000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Robust Data\u2010Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5824-425X","authenticated-orcid":false,"given":"Bin","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-1347","authenticated-orcid":false,"given":"Liyao","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5400-1410","authenticated-orcid":false,"given":"Tao","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8594-5665","authenticated-orcid":false,"given":"Renkang","family":"Geng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9364-5171","authenticated-orcid":false,"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-5141","authenticated-orcid":false,"given":"Ye","family":"Tian","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00449"},{"key":"e_1_2_10_2_2","volume-title":"Internet of Things-IoT Infrastructures, Volume 169","author":"Mandler B.","year":"2016"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3027684):1\u20131"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.03.043"},{"key":"e_1_2_10_5_2","article-title":"Tightly-coupled integration of INS and UWB using fixed-lag extended UFIR smoothing for quadrotor localization","volume":"8","author":"Xu Y.","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_10_6_2","article-title":"Xxx an EEG signal-based music treatment system for autistic children","volume":"2020","author":"Sun M.","year":"2021","journal-title":"Security and Communication Networks"},{"key":"e_1_2_10_7_2","unstructured":"SunB. 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