An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Sivasankari Jothiraj ◽  
Sridevi Balu ◽  
Neelaveni Rangaraj
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Optimization Model ◽  
Cooperative Spectrum Sensing ◽  
Adaptive Threshold ◽  
Radio Networks

scholarly journals A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Muhammad Jibran ◽  
Insoo Koo ◽  
Su Min Kim ◽  
Junsu Kim
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Evidence Theory ◽  
Adaptive Threshold ◽  
Radio Networks ◽  
Maximal Ratio Combining ◽  
Fusion Center ◽  
Adaptive Approach

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Cooperative Spectrum Sensing with Entropy for Mobile Cognitive Radio Networks

2020 ◽  
Author(s):  
Cadena Munoz Ernesto ◽  
Julian Andres Rodriguez Martinez ◽  
Luis Fernando Pedraza Martinez ◽  
Ingrid Patricia Paez Parra
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks

scholarly journals An Active and Passive Reputation Method for Secure Wideband Spectrum Sensing Based on Blockchain

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1346
Author(s):  
Xinyu Xie ◽  
Zhuhua Hu ◽  
Min Chen ◽  
Yaochi Zhao ◽  
Yong Bai
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Sampling Rate ◽  
Radio Networks ◽  
Single Node ◽  
Decision Algorithm ◽  
Collusion Attacks ◽  
Wideband Spectrum Sensing

Spectrum is a kind of non-reproducible scarce strategic resource. A secure wideband spectrum sensing technology provides the possibility for the next generation of ultra-dense, ultra-large-capacity communications to realize the shared utilization of spectrum resources. However, for the open collaborative sensing in cognitive radio networks, the collusion attacks of malicious users greatly affect the accuracy of the sensing results and the security of the entire network. To address this problem, this paper proposes a weighted fusion decision algorithm by using the blockchain technology. The proposed algorithm divides the single-node reputation into active reputation and passive reputation. Through the proposed token threshold concept, the active reputation is set to increase the malicious cost of the node; the passive reputation of the node is determined according to the historical data and recent performance of the blockchain. The final node weight is obtained by considering both kinds of reputation. The proposed scheme can build a trust-free platform for the cognitive radio collaborative networks. Compared with the traditional equal-gain combination algorithm and the centralized sensing algorithm based on the beta reputation system, the simulation results show that the proposed algorithm can obtain reliable sensing results with a lower number of assistants and sampling rate, and can effectively resist malicious users’ collusion attacks. Therefore, the security and the accuracy of cooperative spectrum sensing can be significantly improved in cognitive radio networks.

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