Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning
In order to completely restore WSNs data and improve the quality of data aggregation, a fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning is proposed. The wireless sensor network data is collected under the micro-cluster evolutionary learning, and the Kalman f...
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| Veröffentlicht in: | Evolutionary intelligence 2024, Vol.17 (4), p.2467-2476 |
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| container_title | Evolutionary intelligence |
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| creator | Li, Xiaorong Shu, Zhinian |
| description | In order to completely restore WSNs data and improve the quality of data aggregation, a fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning is proposed. The wireless sensor network data is collected under the micro-cluster evolutionary learning, and the Kalman filter is introduced. According to the linear expression area of the filter in space, the linear regression range of the wireless sensor network data is given, and the dynamic data preprocessing and feature extraction of WSNs are designed. According to the extraction results, the parent node of each node in the tree is set to form a routing tree with the base station as the root node for data forwarding. The features of the target domain are extracted, and input into the range determination layer, and then the WSNs dynamic data are quickly aggregated to realize the rapid aggregation of WSNs dynamic data. The experimental results show that the proposed method has low average entropy, good convergence accuracy, stable data convergence quality and strong repair performance. |
| doi_str_mv | 10.1007/s12065-023-00896-2 |
| format | Article |
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The wireless sensor network data is collected under the micro-cluster evolutionary learning, and the Kalman filter is introduced. According to the linear expression area of the filter in space, the linear regression range of the wireless sensor network data is given, and the dynamic data preprocessing and feature extraction of WSNs are designed. According to the extraction results, the parent node of each node in the tree is set to form a routing tree with the base station as the root node for data forwarding. The features of the target domain are extracted, and input into the range determination layer, and then the WSNs dynamic data are quickly aggregated to realize the rapid aggregation of WSNs dynamic data. The experimental results show that the proposed method has low average entropy, good convergence accuracy, stable data convergence quality and strong repair performance.</description><identifier>ISSN: 1864-5909</identifier><identifier>EISSN: 1864-5917</identifier><identifier>DOI: 10.1007/s12065-023-00896-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applications of Mathematics ; Artificial Intelligence ; Bioinformatics ; Clusters ; Control ; Convergence ; Data management ; Engineering ; Evolution ; Feature extraction ; Kalman filters ; Learning ; Mathematical and Computational Engineering ; Mechatronics ; Nodes ; Research Paper ; Robotics ; Statistical Physics and Dynamical Systems ; Wireless sensor networks</subject><ispartof>Evolutionary intelligence, 2024, Vol.17 (4), p.2467-2476</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. corrected publication 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c314t-e6ed3607b18d32b485521d656f48b0fd83d6b24eec05eac2a965b626cff226053</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12065-023-00896-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12065-023-00896-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Li, Xiaorong</creatorcontrib><creatorcontrib>Shu, Zhinian</creatorcontrib><title>Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning</title><title>Evolutionary intelligence</title><addtitle>Evol. 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The experimental results show that the proposed method has low average entropy, good convergence accuracy, stable data convergence quality and strong repair performance.</description><subject>Applications of Mathematics</subject><subject>Artificial Intelligence</subject><subject>Bioinformatics</subject><subject>Clusters</subject><subject>Control</subject><subject>Convergence</subject><subject>Data management</subject><subject>Engineering</subject><subject>Evolution</subject><subject>Feature extraction</subject><subject>Kalman filters</subject><subject>Learning</subject><subject>Mathematical and Computational Engineering</subject><subject>Mechatronics</subject><subject>Nodes</subject><subject>Research Paper</subject><subject>Robotics</subject><subject>Statistical Physics and Dynamical Systems</subject><subject>Wireless sensor networks</subject><issn>1864-5909</issn><issn>1864-5917</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLxDAQhYMouK7-AU8Bz9FJ0qTtURZXhUUPKuIppM20dum2a9IK--_NWtGbpxmY7715PELOOVxygPQqcAFaMRCSAWS5ZuKAzHimE6Zynh7-7pAfk5MQ1gBaQJrMyNvShoHauvZY26HpO7rB4b13tK_o69NDoG7X2U1TUmcHSwsbMJ4i1JS-Z2U7hgE9xc--Hfdi63e0Reu7pqtPyVFl24BnP3NOXpY3z4s7tnq8vV9cr1gpeTIw1OikhrTgmZOiSDKlBHda6SrJCqhcJp0uRIJYgkJbCptrVWihy6oSQoOSc3Ix-W59_zFiGMy6H30XXxoJaZ7pXEiIlJiomDsEj5XZ-mYT8xoOZl-hmSo0sULzXaERUSQnUYhwV6P_s_5H9QXpnXRj</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Li, Xiaorong</creator><creator>Shu, Zhinian</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2024</creationdate><title>Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning</title><author>Li, Xiaorong ; Shu, Zhinian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c314t-e6ed3607b18d32b485521d656f48b0fd83d6b24eec05eac2a965b626cff226053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Applications of Mathematics</topic><topic>Artificial Intelligence</topic><topic>Bioinformatics</topic><topic>Clusters</topic><topic>Control</topic><topic>Convergence</topic><topic>Data management</topic><topic>Engineering</topic><topic>Evolution</topic><topic>Feature extraction</topic><topic>Kalman filters</topic><topic>Learning</topic><topic>Mathematical and Computational Engineering</topic><topic>Mechatronics</topic><topic>Nodes</topic><topic>Research Paper</topic><topic>Robotics</topic><topic>Statistical Physics and Dynamical Systems</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xiaorong</creatorcontrib><creatorcontrib>Shu, Zhinian</creatorcontrib><collection>CrossRef</collection><jtitle>Evolutionary intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiaorong</au><au>Shu, Zhinian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning</atitle><jtitle>Evolutionary intelligence</jtitle><stitle>Evol. 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The features of the target domain are extracted, and input into the range determination layer, and then the WSNs dynamic data are quickly aggregated to realize the rapid aggregation of WSNs dynamic data. The experimental results show that the proposed method has low average entropy, good convergence accuracy, stable data convergence quality and strong repair performance.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12065-023-00896-2</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Applications of Mathematics Artificial Intelligence Bioinformatics Clusters Control Convergence Data management Engineering Evolution Feature extraction Kalman filters Learning Mathematical and Computational Engineering Mechatronics Nodes Research Paper Robotics Statistical Physics and Dynamical Systems Wireless sensor networks |
| title | Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning |
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