A cooperative spectrum sensing scheme based on task assignment algorithm for cognitive radio networks

Nabil, Mariam; Elsayed, Walaa; ElNainay, Mustafa

doi:10.1109/iwcmc.2014.6906348

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…e artificial bee colony clustering (ABCC) algorithm in [11] is competent to reduce and stabilize the energy expenditure of the cooperative users. In CSS, SUs make their own local decisions about the PU existence and forward it to the fusion center (FC) for further analysis [12,13]. CSS is exposed to the false sensing reports of malicious users (MUs), therefore identification and exclusion of MU reports in the cooperative scheme is essential for minimizing their adverse effects.…”

Section: Introductionmentioning

confidence: 99%

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Gul

Khan

Kim

et al. 2020

Mobile Information Systems

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In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

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Section: Introductionmentioning

confidence: 99%

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Gul

Khan

Kim

et al. 2020

Mobile Information Systems

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“…CSS is investigated as an efficient detection method in the presence of primary user emulation attack in [16]. Priority values are assigned to SUs based on reputation results of each SU in [17][18][19][20] to give less importance to the sensing results of SUs with a reputation below threshold. A reputation-based CSS method is proposed in [21] that improves the system performance by detecting and rejecting the Byzantine malicious users, in order to improve system performance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

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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“…A third architecture commonly associated with CRN is cooperative and non-cooperative architectures. For the cooperative design, the SUs work together to make decisions on such things as spectrum sensing, so that their decisions are usually multilateral and centrally controlled [15,16]. Furthermore, cooperation can be between the PU network and the SU network [17,18].…”

Section: Introductionmentioning

confidence: 99%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources at their disposal in meeting the demands of their numerous users and driving their operations. As a result, a considerable amount of research work has been recently dedicated to investigating and developing resource allocation (RA) models that capture the essentials of CRN. The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. To address this problem and bridge the gap, in this paper, a comprehensive study on the prevalent techniques developed for addressing RA problems in CRN is carried out, with an intent to put some structure, relevance and meaning to the various solution approaches. The solution models are therefore grouped and/or classified based on certain outstanding criteria, and their strengths and weaknesses highlighted. Open-ended problems are identified, and suggestions for improving solution models are given. The study therefore gives good directions for further investigations on developing RA solutions in CRN.

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A cooperative spectrum sensing scheme based on task assignment algorithm for cognitive radio networks

Cited by 10 publications

References 13 publications

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Solving resource allocation problems in cognitive radio networks: a survey

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