Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

doi:10.1109/ACCESS.2019.2963512

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室

	Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing
	Zhang, Shunchao 1; Wang, Yonghua 1,3; Wan, Pin 1,2; Zhuang, Jiawei 1; Zhang, Yongwei 1; Li, Yi 1
刊名	IEEE ACCESS
	2020
卷号	8 页码:5777-5786
关键词	Cognitive radio robust cooperative spectrum sensing sensing data fusion K-medoids clustering algorithm Mean-shift clustering algorithm
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2963512
通讯作者	Wang, Yonghua(wangyonghua@gdut.edu.cn)
英文摘要	In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.
资助项目	Special Funds from the National Natural Science Foundation of China[61971147] ; Central Finance to Support the Development of Local Universities[400170044] ; Central Finance to Support the Development of Local Universities[400180004] ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20180106] ; Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education[MSC-201706A] ; School-Enterprise Collaborative Education Project of Guangdong Province[PROJ1007512221732966400] ; Foundation of National and Local Joint Engineering Research Center of Intelligent Manufacturing Cyber-Physical Systems[008] ; Guangdong Provincial Key Laboratory of Cyber-Physical Systems[008] ; Higher Education Quality Projects of Guangdong Province ; Guangdong University of Technology
WOS关键词	MALICIOUS USER DETECTION ; PERFORMANCE
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000524682100003
资助机构	Special Funds from the National Natural Science Foundation of China ; Central Finance to Support the Development of Local Universities ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education ; School-Enterprise Collaborative Education Project of Guangdong Province ; Foundation of National and Local Joint Engineering Research Center of Intelligent Manufacturing Cyber-Physical Systems ; Guangdong Provincial Key Laboratory of Cyber-Physical Systems ; Higher Education Quality Projects of Guangdong Province ; Guangdong University of Technology
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/38922]
专题	自动化研究所_复杂系统管理与控制国家重点实验室
通讯作者	Wang, Yonghua
作者单位	1.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China 2.South Cent Univ Nationalities, Hubei Key Lab Intelligent Wireless Commun, Wuhan 430074, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shunchao,Wang, Yonghua,Wan, Pin,et al. Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing[J]. IEEE ACCESS,2020,8:5777-5786.
APA	Zhang, Shunchao,Wang, Yonghua,Wan, Pin,Zhuang, Jiawei,Zhang, Yongwei,&Li, Yi.(2020).Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing.IEEE ACCESS,8,5777-5786.
MLA	Zhang, Shunchao,et al."Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing".IEEE ACCESS 8(2020):5777-5786.