{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T14:35:12Z","timestamp":1648737312808},"reference-count":21,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2016,8,1]],"date-time":"2016-08-01T00:00:00Z","timestamp":1470009600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2016,8]]},"abstract":"<jats:p> Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. Existing compressive sensing\u2013based data gathering techniques still follow sampling than compression paradigm. In this article, we proposed a random sampling zero-encoding data gathering scheme for wireless sensor networks, which exploits virtual Gaussian energy diffusion model to obtain sampling and compression data gathering. Our proposed data gathering model not only can make simultaneous sampling and compression but also do not need to assign projection matrix to each sensor node. Our scheme can efficiently resolve two types of sensor networks\u2019 data gathering problems: recover missing sensory data and extend monitoring field using incomplete random sampling. Extensive experimental results show that our proposed random sampling zero-encoding data gathering model has good performance for reconstructing the sensory data in wireless sensor networks. <\/jats:p>","DOI":"10.1177\/1550147716659425","type":"journal-article","created":{"date-parts":[[2016,8,27]],"date-time":"2016-08-27T00:08:31Z","timestamp":1472256511000},"page":"155014771665942","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-effort projection for sensory data reconstruction in wireless sensor networks"],"prefix":"10.1177","volume":"12","author":[{"given":"Xiancun","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Engineering, West Anhui University, Lu\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Information Engineering, West Anhui University, Lu\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,8,25]]},"reference":[{"key":"bibr1-1550147716659425","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2003.814918"},{"key":"bibr2-1550147716659425","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2008.04.002"},{"key":"bibr4-1550147716659425","first-page":"603","volume-title":"Proceedings IEEE INFOCOM, 2012","author":"Wang J"},{"key":"bibr5-1550147716659425","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2010.092810.100063"},{"key":"bibr6-1550147716659425","first-page":"145","volume-title":"Proceedings of the 15th annual international conference on mobile computing and networking","author":"Luo C"},{"key":"bibr8-1550147716659425","first-page":"51","volume-title":"Proceedings of the 5th international conference on information processing in sensor networks","author":"Rabbat M"},{"key":"bibr9-1550147716659425","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914732"},{"key":"bibr10-1550147716659425","doi-asserted-by":"crossref","unstructured":"Lee S, Pattem S, Sathiamoorthy M, Compressed sensing and routing in multi-hop networks. 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