Energy detection based cooperative spectrum sensing using fuzzy conditional entropy maximization

Banerjee, Avik; Maity, Santi P.

doi:10.1109/ants.2014.7057228

Cited by 3 publications

(26 citation statements)

References 16 publications

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“…This multiple threshold‐based solution, on binary hypothesis problem, is explored in the multiclass (multiple) hypothesis problem. The limitations of the related works mentioned in other works are summarized as follows. The system model in the work of Chen et al does not consider the isolated, ie, exclusive presence of PUEA.…”

Section: Related Work On Security In Cssmentioning

confidence: 99%

“…The solution suggested here assigns an individual class to represent each state. This is unlike the multiple classes used to indicate the relative degree of PU signal presence in the work of Banerjee and Maity . Furthermore, in the aforementioned work, the local sensing energy of individual SU was sent to FC and the highest energy was used following the selection combining (SC) scheme.…”

Section: Related Work On Security In Cssmentioning

confidence: 99%

“…Figure shows a pictorial representation to highlight the distinctive differences with related works and the contributions of this work. In brief, the distinctive differences of the present system model over related works as well as the contributions of this work are as follows. A multiclass hypothesis is developed, unlike in the works of Chen et al and Banerjee and Maity, to detect PU signal in presence of PUEA.…”

Section: Related Work On Security In Cssmentioning

confidence: 99%

“…In brief, the distinctive differences of the present system model over related works as well as the contributions of this work are as follows. A multiclass hypothesis is developed, unlike in the works of Chen et al and Banerjee and Maity, to detect PU signal in presence of PUEA. The hypothesis modeling in the present study includes the combination of PU and PUEA, presence of PU signal alone, ie, in absence of PUEA, PU signal in presence of PUEA, PUEA alone, and in noise‐only scenario.…”

Section: Related Work On Security In Cssmentioning

confidence: 99%

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Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

Banerjee

Maity

2019

Trans Emerging Tel Tech

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Cooperative spectrum sensing (CSS) in cognitive radio system shows improved performance on spectrum sensing but, at the same time, faces a common threat called primary user emulation attack (PUEA) that impersonates the primary user (PU). This work explores joint PU and PUEA detection on CSS as a multiclass hypothesis using fuzzy conditional entropy maximization. To meet the goal, a multithreshold approach is suggested using a constrained differential evolution algorithm search to determine an optimal set of fuzzy function parameters, which maximizes the conditional entropy on the energy values. The proposed work also investigates the performance of CSS in presence of a group of malicious users (as PUEA), which generates signals and directly sends the same to the fusion center. Numerical results through simulation illustrate the efficacy of the proposed scheme in terms of both reliable PU detection in presence or absence of PUEA/malicious user and separate detection of falsifying PU and energy consumption in CSS. Simulation results show that the proposed scheme offers ∼17.54% and ∼39.39% higher detection probability of PU over the existing works in presence of PUEA.

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Section: Related Work On Security In Cssmentioning

confidence: 99%

Section: Related Work On Security In Cssmentioning

confidence: 99%

Section: Related Work On Security In Cssmentioning

confidence: 99%

Section: Related Work On Security In Cssmentioning

confidence: 99%

See 2 more Smart Citations

Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

Banerjee

Maity

2019

Trans Emerging Tel Tech

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“…Since the frequency spectrum has been saturated due to the coexistence of different technologies and standards, the provision of opportunistic spectrum access for sensors in CRSNs is an important task . In such networks unlicensed cognitive radio users (ie, sensors) or Secondary Users (SUs) can use the frequency spectrum that belongs to some licensed Primary Users (PUs), if they do not cause harmful interference with PUs . The application of CRSNs can be found in the paradigm of Internet of Things (IoT).…”

Section: Introductionmentioning

confidence: 99%

Improving the lifetime of multichannel cognitive radio sensor networks via new spectrum sensing method

Monemian

Mahdavi

Omidi

2018

Trans Emerging Tel Tech

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Cognitive Radio Sensor Networks (CRSNs) are wireless sensor networks implemented with cognitive radio capability to provide spectrum resources for sensors in an opportunistic manner. The application of CRSNs can be found in the paradigm of Internet of Things (IoT) where each sensor represents an object in the IoT paradigm. In order to prevent simultaneous transmissions of Primary Users (PUs) and CRSN nodes, sensors should be aware of the presence of PUs. One of the most common methods for making the sensors aware of the presence of PUs is consecutive spectrum sensing. Since the sensors have the duty of both sensing environment and transmitting the results, their battery lives are highly prone to rapid drain. In addition, the lack of effective management of sensor operations for spectrum sensing may lead to short longevity of the network. In this paper, the energy consumption process of sensors in such networks has been analyzed and an energy‐based algorithm is proposed to appropriately select sensors based on their detection probabilities for spectrum sensing. The analytical and numerical results confirm the improved lifetime and performance based on our proposed algorithm in comparison with the conventional ones. Moreover, the computational complexity of the proposed algorithm is significantly lower than that of the existing methods.

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Techniques for dealing with uncertainty in cognitive radio networks

Salahdine

Kaabouch

Ghazi

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. However, due to multipath fading, shadowing, or varying channel conditions, uncertainty affects the cognitive cycle processes, measurements, decisions, and actions. In the observing step, measurements (i.e., information) taken by the secondary users (SUs) are uncertain. In the next step, the SUs make decisions based on what has already been observed using their knowledge bases, which may have been impacted by the uncertainty, leading to wrong decisions. In the last step, uncertainty can affect the decision of the cognitive radio system, which sometimes can lead to the wrong action. Thus, the uncertainty propagation influences the cognitive radio performance. Therefore, mitigating the uncertainty in the cognitive cycle is a necessity. This paper provides a deep overview of techniques that handle uncertainty in cognitive radio networks.

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Energy detection based cooperative spectrum sensing using fuzzy conditional entropy maximization

Cited by 3 publications

References 16 publications

Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

Improving the lifetime of multichannel cognitive radio sensor networks via new spectrum sensing method

Techniques for dealing with uncertainty in cognitive radio networks

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