Cooperative spectrum sensing using fuzzy logic for cognitive radio network

Rasheed, Tallataf; Rashdi, Adnan; Akhtar, Ahmad Naeem

doi:10.1109/icaset.2018.8376914

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…The CR network dynamically accesses the present spectrum when a primary user is not utilizing it. The FL-CSS scheme shows better spectrum sensing results [5]. Due to the highly dynamic nature of VANET, CR faces some instability issues.…”

Section: Related Work and Backgroundmentioning

confidence: 96%

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Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

et al. 2019

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The Vehicular Ad Hoc Network (VANET) plays a vital role in the development of smart cities, especially in ensuring vehicles' safety on roads. However, VANET wireless-based networks face some challenges such as security, stability, communication, and reliability. To resolve these issues, we propose a fuzzy cluster head selection scheme in Cognitive Radio (CR) VANET, which uses the CR technology for the spectrum sensing algorithm. In this technology, the free spectrums of the primary user are utilized by secondary users without any correlation. Moreover, we have considered some input parameters such as vehicles' average velocity, distance, network connectivity level, lane weight and trustworthiness for the fuzzy system based CR VANET in this research. The selected cluster head provides stability and reliability to the cluster compared to the state of art techniques. Extensive experiments were conducted in order to evaluate the effectiveness of the proposed approach. However, simulation results authenticate more stable and secure cluster formation using the proposed fuzzy logic based CR VANET.

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Section: Related Work and Backgroundmentioning

confidence: 96%

“…State of the art lowest ID clustering algorithm is proposed for the CH selection [1], highest degree clustering schemes [2], weighted clustering schemes [3], [4], Affinity propagation algorithm [5], and fuzzy logic-based algorithms [6], [7]. The earlier work has an issue with CH stability.…”

Section: Problem Statementmentioning

confidence: 99%

Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

et al. 2019

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“…Both energy efficiency and enhance network lifetime were considered based on three factors of the node's remaining energy, the distribution of neighboring nodes or node degree, and the closest node to the base station. The aim was to decrease energy consumption and increase network lifetime (Rasheed et al, 2018). Finally, a fuzzy-based CSS to improve the probability of spectrum detection in a harsh smart grid environment was developed in Nassef and Alhebshi (2016).…”

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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“…However, the spectrum sensing performance of the energy detector is severely affected by destructive channel effects such as shadowing and fading, and noise. To minimize the negative effects caused by noise uncertainty and communication channel, the cooperative spectrum sensing model is defined in the literature [35], [36]. In [35], the researchers proposed a fuzzy logic-based perception format for collaborative energy detection, based on the new reliability factors for local spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing in Cognitive Radio Networks: Threshold Optimization and Analysis

Koçkaya

Develı

2020

Preprint

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Cognitive radio (CR) is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection and matched filter based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio (SNR) regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.

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Cooperative spectrum sensing using fuzzy logic for cognitive radio network

Cited by 14 publications

References 13 publications

Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

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

Spectrum Sensing in Cognitive Radio Networks: Threshold Optimization and Analysis

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