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Symmetry 2017, 9(7), 110;

User Classification in Crowdsourcing-Based Cooperative Spectrum Sensing

1,2 and 1,*
School of Computer Science and Technology, Shandong University, Jinan 250100, China
Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan 250014, China
Author to whom correspondence should be addressed.
Academic Editor: Chi-Hua Chen
Received: 21 May 2017 / Revised: 3 July 2017 / Accepted: 3 July 2017 / Published: 6 July 2017
(This article belongs to the Special Issue Applications of Internet of Things)
PDF [677 KB, uploaded 7 July 2017]


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. View Full-Text
Keywords: classification; crowdsourcing; sensing; distributed classification; crowdsourcing; sensing; distributed

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Zhai, L.; Wang, H. User Classification in Crowdsourcing-Based Cooperative Spectrum Sensing. Symmetry 2017, 9, 110.

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