{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:53Z","timestamp":1760242433613,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,6]],"date-time":"2017-07-06T00:00:00Z","timestamp":1499299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious). There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.<\/jats:p>","DOI":"10.3390\/sym9070110","type":"journal-article","created":{"date-parts":[[2017,7,6]],"date-time":"2017-07-06T10:55:45Z","timestamp":1499338545000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["User Classification in Crowdsourcing-Based Cooperative Spectrum Sensing"],"prefix":"10.3390","volume":"9","author":[{"given":"Linbo","family":"Zhai","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Jinan 250100, China"},{"name":"Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Jinan 250100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,6]]},"reference":[{"key":"ref_1","unstructured":"Cisco (2014). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014, Tech., Cisco."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.telpol.2016.01.003","article-title":"White space: Definitional perspectives and their role in exploiting spectrum opportunities","volume":"40","author":"Akhtar","year":"2016","journal-title":"Telecommun. Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2014.04.001","article-title":"Primary radio user activity models for cognitive radio networks: A survey","volume":"43","author":"Saleem","year":"2014","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/SURV.2009.090109","article-title":"A survey of spectrum sensing algorithms for cognitive radio applications","volume":"11","author":"Yucek","year":"2009","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","first-page":"1","article-title":"A survey on security threats and detection techniques in cognitive radio networks","volume":"14","author":"Fragkiadakis","year":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/JSAC.2014.140301","article-title":"Fast Detection of Malicious Behavior in Cooperative Spectrum Sensing","volume":"32","author":"Soltanmohammadi","year":"2014","journal-title":"IEEE J. Sel. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TMC.2010.252","article-title":"Secure cooperative sensing in IEEE 802.22 WRANS using shadow fading correlation","volume":"10","author":"Min","year":"2011","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/TMC.2015.2398446","article-title":"Thwarting Intelligent Malicious Behaviors in Cooperative Spectrum Sensing","volume":"14","author":"Wang","year":"2015","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/JSAC.2012.121009","article-title":"Attack prevention for collaborative spectrum sensing in cognitive radio networks","volume":"30","author":"Duan","year":"2012","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/TSP.2010.2091277","article-title":"Collaborative spectrum sensing in the presence of byzantine attacks in cognitive radio networks","volume":"59","author":"Rawat","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1109\/TSP.2011.2181839","article-title":"Detecting and counteracting statistical attacks in cooperative spectrum sensing","volume":"60","author":"Penna","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.compeleceng.2015.05.015","article-title":"A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks","volume":"52","author":"Reyes","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Manesh, M., Apu, M., Kaabouch, N., and Hu, W. (2016, January 20\u201322). Performance evaluation of spectrum sensing techniques for cognitive radio systems. Proceedings of the IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON.2016.7777829"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Arjoune, Y., Kaabouch, N., Ghazi, H., and Tamtaoui, A. (2017, January 9\u201311). Compressive sensing: Performance comparison of sparse recovery algorithms. Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2017.7868430"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5584","DOI":"10.1109\/TVT.2015.2487682","article-title":"Dynamic Performance Analysis of Uplink Transmission in Cluster-Based Heterogeneous Vehicular Networks","volume":"64","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/TCOMM.2015.2500559","article-title":"Incumbent and LSA Licensee Classification Through Distributed Cognitive Networks","volume":"64","author":"Sobron","year":"2016","journal-title":"IEEE Trans. Commun."},{"key":"ref_17","unstructured":"Falkenauer, E. (1998). Genetic Algorithms and Grouping Problems, Wiley."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:40Z","timestamp":1760208100000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,6]]},"references-count":17,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2017,7]]}},"alternative-id":["sym9070110"],"URL":"https:\/\/doi.org\/10.3390\/sym9070110","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2017,7,6]]}}}