{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T04:40:02Z","timestamp":1742272802256,"version":"3.40.1"},"reference-count":26,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2012,1,1]],"date-time":"2012-01-01T00:00:00Z","timestamp":1325376000000},"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":[[2012,1,1]]},"abstract":"<jats:p>Source localization is an important problem in wireless sensor networks (WSNs). An exciting state-of-the-art algorithm for this problem is maximum likelihood (ML), which has sufficient spatial samples and consumes much energy. In this paper, an effective method based on compressed sensing (CS) is proposed for multiple source locations in received signal strength-wireless sensor networks (RSS-WSNs). This algorithm models unknown multiple source positions as a sparse vector by constructing redundant dictionaries. Thus, source parameters, such as source positions and energy, can be estimated by [Formula: see text]-norm minimization. To speed up the algorithm, an effective construction of multiresolution dictionary is introduced. Furthermore, to improve the capacity of resolving two sources that are close to each other, the adaptive dictionary refinement and the optimization of the redundant dictionary arrangement (RDA) are utilized. Compared to ML methods, such as alternating projection, the CS algorithm can improve the resolution of multiple sources and reduce spatial samples of WSNs. The simulations results demonstrate the performance of this algorithm.<\/jats:p>","DOI":"10.1155\/2012\/592471","type":"journal-article","created":{"date-parts":[[2012,1,6]],"date-time":"2012-01-06T08:31:49Z","timestamp":1325838709000},"page":"592471","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks"],"prefix":"10.1177","volume":"8","author":[{"given":"Lei","family":"Liu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Graduate University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Song","family":"Chong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Hong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2012,1,5]]},"reference":[{"key":"B1-2012-592471","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2005.05.033"},{"key":"B2-2012-592471","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2008.04.002"},{"key":"B3-2012-592471","doi-asserted-by":"publisher","DOI":"10.1155\/S1110865703212075"},{"key":"B4-2012-592471","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2002.985672"},{"key":"B5-2012-592471","unstructured":"Malioutov D. 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