Performance improvements of cooperative spectrum sensing in cognitive radio networks with correlated cognitive users

Benedetto, Francesco; Giunta, Gaetano; Tedeschi, Antonio; Guzzon, E.

doi:10.1109/tsp.2015.7296397

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Many application scenarios, such as e-commerce [36], peerto-peer file-sharing [37], crowd sensing [38], social networks [39], [40], [41] are becoming more reliant on the trust mechanism. The trust-mechanism furthermore plays a key role in CSS, such as 1) assisting FC in making reliable decisions, 2) encouraging honest behavior, and 3) discouraging attackers from participating.…”

Section: Trust Mechanismmentioning

confidence: 99%

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Rao¹,

Prasad²,

Prasad³

2022

IJRITCC

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The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.

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Section: Trust Mechanismmentioning

confidence: 99%

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Rao¹,

Prasad²,

Prasad³

2022

IJRITCC

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“…The FC determines absolute differences of the sensing results of the jth SU with the average sensing energy provided by all other SUs based on the result in (8). The average of all SU decisions is calculated by neglecting the jth SU result in the ith sensing interval to find out the impact of not including this particular user in the collective sensing result.…”

Section: Best Sensing Report Selection Using Genetic Algorithm (Ga)mentioning

confidence: 99%

“…The strong fading or shadowing effects lead to some correlated observations to be under the threshold of the conventional energy detector, which degrades the detection probability. An innovative test is proposed in [8] for the case of correlated observations, in order to improve the detection performance. In centralized cooperative spectrum sensing (CSS), all SUs perform individual spectrum sensing and share this information with the FC.…”

Section: Introductionmentioning

confidence: 99%

“…Kullback-Leibler divergencebased CSS is proposed in [32] to protect the CSS from the spectrum sensing data falsification (SSDF) attack [33] of the always yes and always no MUs and work fine in case of a large number of MUs. In the hard decision fusion (HDF) as in [8,[34][35][36], SUs send a hard binary decision of the PU channel to the FC. These hard decisions are fused together by the FC in counting and voting rules in [37].…”

Section: Introductionmentioning

confidence: 99%

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Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

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In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

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