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This paper investigates the issue of robust and efficient cooperative spectrum sensing in CVNs. We propose robust cooperative spectrum sensing via low\u2010rank matrix recovery (LRMR\u2010RCSS) in cognitive vehicular networks to address the uncertainty of the quality of potentially corrupted sensing data by utilizing the real spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low\u2010rank and joint\u2010sparse structure. Considering that the sensing data from crowd cognitive vehicles would be vast, we extend our robust cooperative spectrum sensing algorithm to dense cognitive vehicular networks via weighted low\u2010rank matrix recovery (WLRMR\u2010RCSS) to reduce the complexity of cooperative spectrum sensing. In the WLRMR\u2010RCSS algorithm, we propose a correlation\u2010aware selection and weight assignment scheme to take advantage of secondary user (SU) diversity and reduce the cooperation overhead. Extensive simulation results demonstrate that the proposed LRMR\u2010RCSS and WLRMR\u2010RCSS algorithms have good performance in resisting malicious SU behavior. Moreover, the simulations demonstrate that the proposed WLRMR\u2010RCSS algorithm could be successfully applied to a dense traffic environment.<\/jats:p>","DOI":"10.1155\/2018\/6319378","type":"journal-article","created":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T23:32:31Z","timestamp":1530055951000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust and Low\u2010Complexity Cooperative Spectrum Sensing via Low\u2010Rank Matrix Recovery in Cognitive Vehicular Networks"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5682-0521","authenticated-orcid":false,"given":"Xia","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhimin","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caili","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,6,26]]},"reference":[{"key":"e_1_2_10_1_2","unstructured":"World Health Statistics World Health Organizaton 2016."},{"key":"e_1_2_10_2_2","first-page":"1","article-title":"IEEE standard for wireless access in vehicular environments (WAVE)\u2014multi-channel operation","year":"2016","journal-title":"IEEE Std 1609.4-2016 (Revision of IEEE Std 1609.4-2010)"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2013.6515061"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2013.175"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"XiongK. 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