{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T01:05:21Z","timestamp":1783559121826,"version":"3.55.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["No. 21JP115"],"award-info":[{"award-number":["No. 21JP115"]}]},{"name":"International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province","award":["Grant No. 2018KW-049"],"award-info":[{"award-number":["Grant No. 2018KW-049"]}]},{"name":"Communication Soft Science Program of Ministry of Industry and Information Technology","award":["Grant No. 2019-R-29"],"award-info":[{"award-number":["Grant No. 2019-R-29"]}]},{"name":"Science and Technology Project in Shaanxi Province of China","award":["No.2019ZDLGY07-08"],"award-info":[{"award-number":["No.2019ZDLGY07-08"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11276-021-02852-3","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:03:33Z","timestamp":1641686613000},"page":"393-411","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An adaptive sliding window for anomaly detection of time series in wireless sensor networks"],"prefix":"10.1007","volume":"28","author":[{"given":"Zhongmin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0902-4776","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingwu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"issue":"2","key":"2852_CR1","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s11276-019-02197-y","volume":"27","author":"S Saraswathi","year":"2019","unstructured":"Saraswathi, S., Suresh, G. R., & Katiravan, J. (2019). False alarm detection using dynamic threshold in medical wireless sensor networks. Wireless Networks, 27(2), 925\u2013937. https:\/\/doi.org\/10.1007\/s11276-019-02197-y","journal-title":"Wireless Networks"},{"issue":"1","key":"2852_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/TII.2015.2469644","volume":"12","author":"JH Lee","year":"2015","unstructured":"Lee, J. H., Kim, L. H., & Kwon, T. (2015). Flexicast: Energy-efficient software integrity checks to build secure industrial wireless active sensor networks. IEEE Transactions on Industrial Informatics, 12(1), 6\u201314. https:\/\/doi.org\/10.1109\/TII.2015.2469644","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2852_CR3","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.compag.2018.06.046","volume":"151","author":"F Kiani","year":"2018","unstructured":"Kiani, F. (2018). Animal behavior management by energy-efficient wireless sensor networks. Computers and Electronics in Agriculture, 151, 478\u2013484. https:\/\/doi.org\/10.1016\/j.compag.2018.06.046","journal-title":"Computers and Electronics in Agriculture"},{"key":"2852_CR4","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.comnet.2017.10.007","volume":"129","author":"A Ayadi","year":"2017","unstructured":"Ayadi, A., Ghorbel, O., Obeid, A. M., & Abid, M. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129, 319\u2013333. https:\/\/doi.org\/10.1016\/j.comnet.2017.10.007","journal-title":"Computer Networks"},{"key":"2852_CR5","unstructured":"Peng, S. (2019). Research on abnormal data detection algorithm in wireless sensor networks. Huaqiao University."},{"key":"2852_CR6","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Zhang, Y., Zhu, X. X., & Zuo, J. (2019). Research on time series anomaly detection algorithm and application. In IEEE 4th advanced information technology, electronic and automation control conference (IAEAC) (Vol. 1, pp. 16\u201320). IEEE. https:\/\/doi.org\/10.1109\/IAEAC47372.2019.8997819","DOI":"10.1109\/IAEAC47372.2019.8997819"},{"key":"2852_CR7","doi-asserted-by":"crossref","unstructured":"Hawkins, D. M. (1980). Identification of outliers (pp. 54\u201370). Chapman and Hall.","DOI":"10.1007\/978-94-015-3994-4"},{"key":"2852_CR8","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.aeue.2017.06.002","volume":"79","author":"M Wu","year":"2017","unstructured":"Wu, M., & Tan, L. (2017). An adaptive distributed parameter estimation approach in incremental cooperative wireless sensor networks. AEU-International Journal of Electronics and Communications, 79, 307\u2013316. https:\/\/doi.org\/10.1016\/j.aeue.2017.06.002","journal-title":"AEU-International Journal of Electronics and Communications"},{"issue":"2\u20134","key":"2852_CR9","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s40012-016-0100-5","volume":"4","author":"K Chandel","year":"2016","unstructured":"Chandel, K., Kunwar, V., Sabitha, S., Choudhury, T., & Mukherjee, S. (2016). A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques. CSI Transactions on ICT, 4(2\u20134), 313\u2013319.","journal-title":"CSI Transactions on ICT"},{"key":"2852_CR10","doi-asserted-by":"crossref","unstructured":"Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002). Enhancing effectiveness of outlier detections for low density patterns (pp. 535\u2013548). Springer.","DOI":"10.1007\/3-540-47887-6_53"},{"key":"2852_CR11","doi-asserted-by":"publisher","first-page":"106919","DOI":"10.1016\/j.asoc.2020.106919","volume":"100","author":"J Li","year":"2021","unstructured":"Li, J., Izakian, H., Pedrycz, W., & Jamal, I. (2021). Clustering-based anomaly detection in multivariate time series data. Applied Soft Computing, 100, 106919. https:\/\/doi.org\/10.1016\/j.asoc.2020.106919","journal-title":"Applied Soft Computing"},{"key":"2852_CR12","doi-asserted-by":"publisher","unstructured":"Kieu, T., Yang, B., & Jensen, C. S. (2018). Outlier detection for multidimensional time series using deep neural networks. In 19th IEEE international conference on mobile data management (MDM) (pp. 125\u2013134). IEEE. https:\/\/doi.org\/10.1109\/MDM.2018.00029","DOI":"10.1109\/MDM.2018.00029"},{"key":"2852_CR13","doi-asserted-by":"publisher","unstructured":"Solberg, H. E., & Lahti, A. (2005). Detection of outliers in reference distributions: Performance of Horn\u2019s algorithm. Clinical Chemistry, 51(12), 2326\u20132332. https:\/\/doi.org\/10.1373\/clinchem.2005.058339","DOI":"10.1373\/clinchem.2005.058339"},{"issue":"1\u20132","key":"2852_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.cca.2006.03.033","volume":"372","author":"Q Zhou","year":"2006","unstructured":"Zhou, Q., Li, S., Li, X., Wang, W., & Wang, Z. (2006). Detection of outliers and establishment of targets in external quality assessment programs. Clinica Chimica Acta, 372(1\u20132), 94\u201397. https:\/\/doi.org\/10.1016\/j.cca.2006.03.033","journal-title":"Clinica Chimica Acta"},{"key":"2852_CR15","unstructured":"Knorr, E. M., & Ng, R. T. (1997). A unified notion of outliers: Properties and computation. In KDD (pp. 219\u2013222)."},{"key":"2852_CR16","doi-asserted-by":"publisher","unstructured":"Breunig, M. M., Kriegel, H. P., Ng, R., & T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on management of data (pp. 93\u2013104). https:\/\/doi.org\/10.1145\/342009.335388","DOI":"10.1145\/342009.335388"},{"key":"2852_CR17","doi-asserted-by":"publisher","unstructured":"Izakian, H., & Pedrycz, W. (2013). Anomaly detection in time series data using a fuzzy c-means clustering. In Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA\/NAFIPS) (pp. 1513\u20131518). IEEE. https:\/\/doi.org\/10.1109\/IFSA-NAFIPS.2013.6608627","DOI":"10.1109\/IFSA-NAFIPS.2013.6608627"},{"key":"2852_CR18","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/ACCESS.2018.2886457","volume":"7","author":"M Munir","year":"2018","unstructured":"Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2018). DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. IEEE Access, 7, 1991\u20132005. https:\/\/doi.org\/10.1109\/ACCESS.2018.2886457","journal-title":"IEEE Access"},{"key":"2852_CR19","doi-asserted-by":"publisher","unstructured":"Lin, S., Clark, R., Birke, R., Sch\u00f6nborn, S., Trigoni, N., & Roberts, S. (2020). Anomaly detection for time series using VAE-LSTM hybrid model. In ICASSP IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4322\u20134326). IEEE. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053558","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"issue":"2","key":"2852_CR20","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.14778\/1454159.1454226","volume":"1","author":"H Ding","year":"2008","unstructured":"Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008). Querying and mining of time series data: Experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment, 1(2), 1542\u20131552. https:\/\/doi.org\/10.14778\/1454159.1454226","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"2","key":"2852_CR21","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1145\/191843.191925","volume":"23","author":"C Faloutsos","year":"1994","unstructured":"Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. ACM Sigmod Record, 23(2), 419\u2013429. https:\/\/doi.org\/10.1145\/191843.191925","journal-title":"ACM Sigmod Record"},{"key":"2852_CR22","doi-asserted-by":"publisher","unstructured":"Agrawal, R., Faloutsos, C., & Swami, A. (1993). Efficient similarity search in sequence databases. In International conference on foundations of data organization and algorithms (pp. 69\u201384). Springer. https:\/\/doi.org\/10.1007\/3-540-57301-1-5","DOI":"10.1007\/3-540-57301-1-5"},{"issue":"10","key":"2852_CR23","doi-asserted-by":"publisher","first-page":"5118","DOI":"10.1109\/TSP.2010.2053028","volume":"58","author":"J Zhong","year":"2010","unstructured":"Zhong, J., & Huang, Y. (2010). Time-frequency representation based on an adaptive short-time Fourier transform. IEEE Transactions on Signal Processing, 58(10), 5118\u20135128. https:\/\/doi.org\/10.1109\/TSP.2010.2053028","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"3","key":"2852_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1023\/A:1008901226235","volume":"8","author":"AT Walden","year":"1998","unstructured":"Walden, A. T., & Cristan, A. C. (1998). Matching pursuit by undecimated discrete wavelet transform for non-stationary time series of arbitrary length. Statistics and Computing, 8(3), 205\u2013219. https:\/\/doi.org\/10.1023\/A:1008901226235","journal-title":"Statistics and Computing"},{"issue":"4","key":"2852_CR25","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1142\/S0219622008003204","volume":"7","author":"Y Peng","year":"2008","unstructured":"Peng, Y., Kou, G., Shi, Y., & Chen, Z. (2008). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology and Decision Making, 7(4), 639\u2013682. https:\/\/doi.org\/10.1142\/S0219622008003204","journal-title":"International Journal of Information Technology and Decision Making"},{"issue":"3","key":"2852_CR26","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001). Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3(3), 263\u2013286.","journal-title":"Knowledge and Information Systems"},{"key":"2852_CR27","doi-asserted-by":"publisher","unstructured":"Lkhagva, B., Suzuki, Y., & Kawagoe, K. (2006). New time series data representation ESAX for financial applications. In 22nd International Conference on Data Engineering Workshops (ICDEW\u201906) (pp. x115\u2013x115). IEEE. https:\/\/doi.org\/10.1109\/ICDEW.2006.99","DOI":"10.1109\/ICDEW.2006.99"},{"key":"2852_CR28","doi-asserted-by":"publisher","unstructured":"Zan, C. T., & Yamana, H. (2016). An improved symbolic aggregate approximation distance measure based on its statistical features. In Proceedings of the 18th international conference on information integration and web-based applications and services (pp. 72\u201380). https:\/\/doi.org\/10.1145\/3011141.3011146","DOI":"10.1145\/3011141.3011146"},{"key":"2852_CR29","doi-asserted-by":"crossref","unstructured":"Elsworth, S., & G\u00fcttel, S. (2020). ABBA: Adaptive Brownianbridge-based symbolic aggregation of time series. Data Mining and Knowledge Discovery, 34(4), 1175\u20131200.","DOI":"10.1007\/s10618-020-00689-6"},{"issue":"19","key":"2852_CR30","doi-asserted-by":"publisher","first-page":"13481","DOI":"10.1007\/s11042-019-08440-0","volume":"79","author":"H Chen","year":"2020","unstructured":"Chen, H., Du, J., Zhang, W., & Li, B. (2020). An iterative end point fitting based trend segmentation representation of time series and its distance measure. Multimedia Tools and Applications, 79(19), 13481\u201313499. https:\/\/doi.org\/10.1007\/s11042-019-08440-0","journal-title":"Multimedia Tools and Applications"},{"key":"2852_CR31","doi-asserted-by":"publisher","unstructured":"Hung, N. Q. V., & Anh, D. T. (2008). An improvement of PAA for dimensionality reduction in large time series databases. In Pacific rim international conference on artificial intelligence (pp. 698\u2013707). Springer. https:\/\/doi.org\/10.1007\/978-3-540-89197-0-64","DOI":"10.1007\/978-3-540-89197-0-64"},{"key":"2852_CR32","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.knosys.2017.07.021","volume":"135","author":"H Ren","year":"2017","unstructured":"Ren, H., Liu, M., Li, Z., & Pedrycz, W. (2017). A piecewise aggregate pattern representation approach for anomaly detection in time series. Knowledge-Based Systems, 135, 29\u201339. https:\/\/doi.org\/10.1016\/j.knosys.2017.07.021","journal-title":"Knowledge-Based Systems"},{"key":"2852_CR33","unstructured":"Keogh, E. J., & Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. In Kdd (pp. 24\u201330)."},{"issue":"23","key":"2852_CR34","first-page":"14","volume":"34","author":"D Zhou","year":"2008","unstructured":"Zhou, D., & Li, M. Q. (2008). Time series segmentation based on series importance point. Computer Engineering, 34(23), 14\u201316.","journal-title":"Computer Engineering"},{"key":"2852_CR35","doi-asserted-by":"publisher","unstructured":"Qu, Y., Wang, C., & Wang, X. S. (1998). Supporting fast search in time series for movement patterns in multiple scales. In Proceedings of the seventh international conference on information and knowledge management (pp. 251\u2013258). https:\/\/doi.org\/10.1145\/288627.288664","DOI":"10.1145\/288627.288664"},{"key":"2852_CR36","unstructured":"Keogh, E. J., & Pazzani, M. J. (1998). An enhanced representation of time series which allows fast and accurate classification, Clustering and Relevance Feedback. In Kdd (pp. 239\u2013243)."},{"key":"2852_CR37","doi-asserted-by":"publisher","unstructured":"Park, S., Lee, D., & Chu, W. W. (1999). Fast retrieval of similar subsequences in long sequence databases. In Proceedings 1999 workshop on knowledge and data engineering exchange (KDEX\u201999) (Cat. No. PR00453) (pp. 60\u201367). IEEE. https:\/\/doi.org\/10.1109\/KDEX.1999.836610","DOI":"10.1109\/KDEX.1999.836610"},{"key":"2852_CR38","unstructured":"Hui, X. (2005). Similarity search and outlier detection in time series. Fudan University."},{"issue":"8","key":"2852_CR39","first-page":"2897","volume":"28","author":"ZY Li","year":"2011","unstructured":"Li, Z. Y., Chen, J., Wang, L. N., & Yang, S. (2011). Abnormity mining based on error and key-point in seismic precursory observation data. Jisuanji Yingyong Yanjiu, 28(8), 2897\u2013290.","journal-title":"Jisuanji Yingyong Yanjiu"},{"key":"2852_CR40","unstructured":"Jia, P. T., & Lin, H. H. C. (2008). Adaptive piecewise linear representation of time series based on error restricted. Computer Engineering and Applications, 44(5), 10\u201313."},{"issue":"11","key":"2852_CR41","doi-asserted-by":"publisher","first-page":"3154","DOI":"10.1117\/12.213617","volume":"34","author":"TA Wilson","year":"1995","unstructured":"Wilson, T. A., Rogers, S. K., & Myers, L. R., Jr. (1995). Perceptual-based hyperspectral image fusion using multiresolution analysis. Optical Engineering, 34(11), 3154\u20133164. https:\/\/doi.org\/10.1117\/12.213617","journal-title":"Optical Engineering"},{"key":"2852_CR42","unstructured":"Bodik, P., Hong, W., & Guestrin, C., Madden, S., Paskin, M., & Thibaux, R. (2004). Intel lab data Online dataset. Retrieved June 2, 2004, from http:\/\/db.csail.mit.edu\/labdata\/labdata.html"},{"key":"2852_CR43","doi-asserted-by":"crossref","unstructured":"Wehrle, K., G\u00fcne, Mesut, & Gross, J. (2010). Modeling and tools for network simulation. Springer.","DOI":"10.1007\/978-3-642-12331-3"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02852-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-021-02852-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02852-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T08:51:37Z","timestamp":1642582297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-021-02852-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2852"],"URL":"https:\/\/doi.org\/10.1007\/s11276-021-02852-3","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"16 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}