{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T12:00:26Z","timestamp":1719230426567},"reference-count":27,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2013,1,25]],"date-time":"2013-01-25T00:00:00Z","timestamp":1359072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J Communication"],"published-print":{"date-parts":[[2014,11]]},"abstract":"SUMMARY<\/jats:title>In order to improve energy efficiency in the process of gathering data and transmitting information, compressed sensing (CS) has been applied increasingly in wireless local area networks (WLANs). In traditional usages of CS techniques in previous literatures, the sparsities of the signals have to be known beforehand, which is significant for the recovered results. However, it is difficult to realize precisely the structures of the actual signals in WLANs. Therefore, it is important to further exploit the reasonable practicality of signals in actual applications. In this paper, we present a spatial\u2013temporal correlation model to optimize measure matrix of CS on the basis of a local region. Furthermore, two algorithms based on two kinds of situations are designed, both of which are pervasive data distributions in real environments. Our algorithms have been proven to be valuable and could be considered for actual applications. Experiments show that the signals could be reconstructed accurately and stably even if their sparsities could not be known in advance. Copyright \u00a9 2013 John Wiley & Sons, Ltd.<\/jats:p>","DOI":"10.1002\/dac.2501","type":"journal-article","created":{"date-parts":[[2013,1,25]],"date-time":"2013-01-25T13:08:53Z","timestamp":1359119333000},"page":"2723-2743","source":"Crossref","is-referenced-by-count":8,"title":["Distributed compressed sensing in wireless local area networks"],"prefix":"10.1002","volume":"27","author":[{"given":"Hao","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology University of Science and Technology of China Hefei Anhu China"},{"name":"Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou Jiangsu China"}]},{"given":"Liusheng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology University of Science and Technology of China Hefei Anhu China"},{"name":"Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou Jiangsu China"}]},{"given":"Hongli","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology University of Science and Technology of China Hefei Anhu China"},{"name":"Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou Jiangsu China"}]},{"given":"An","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology University of Science and Technology of China Hefei Anhu China"},{"name":"Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou Jiangsu China"}]}],"member":"311","published-online":{"date-parts":[[2013,1,25]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.1045"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.1301"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.1181"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"LuoC WuF SunJet al.Compressive data gathering for large\u2010scale wireless sensor networks. 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