SMCSS: A Quick and Reliable Cooperative Spectrum Sensing Scheme for Cognitive Industrial Wireless Networks

Zheng, Meng; Liang, Wei; Huang, Yu; Song, Min

doi:10.1109/access.2016.2641471

Cited by 28 publications

(9 citation statements)

References 47 publications

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“…Deng et al [25] considered optimal sensor scheduling to realize energyefficient CSS. Zheng [26] considered OFDM modulation to send each SU's result to the FC. Li [27] proposed a kind of random broadcast scheme in exchanging the sensing results to reduce the signaling cost in reporting phase, while in similar method, Noh [28] proposed controlling the reporting order of the local test statistics to reduce the reporting time.…”

Section: Related Workmentioning

confidence: 99%

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

Alhussein

Sofotasios

et al. 2020

IEEE Trans. Sustain. Comput.

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The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corresponding probability of detection, probability of false alarm and secondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.

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Section: Related Workmentioning

confidence: 99%

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

Alhussein

Sofotasios

et al. 2020

IEEE Trans. Sustain. Comput.

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“…A sequential CSS in the presence of dynamic byzantine attack for mobile networks to defend against dynamic SDFF attacks is designed in Wu et al (2018), where consistency of individual sensing report is checked. Reputation management combined with subcarrier modulation-based CSS scheme is presented in Zheng et al (2016) to achieve robustness against unreliable subcarriers due to harsh industrial environments. Both trust and reputation are used in Bennaceur et al (2018) to secure the network against SSDF.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

Nassef¹,

Alhebshi²

2021

Int. j. adv. appl. sci.

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Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

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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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SMCSS: A Quick and Reliable Cooperative Spectrum Sensing Scheme for Cognitive Industrial Wireless Networks

Cited by 28 publications

References 47 publications

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

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

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