Joint Selection for Cooperative Spectrum Sensing in Wireless Sensor Networks

Peng, Yuyang; Al-Hazemi, Fawaz; Kim, Heejae; Youn, Chan‐Hyun

doi:10.1109/jsen.2016.2608846

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…The sensor node participates in CSS and sends the local result 0 or 1, which indicates the channel being idle and occupied, to FC for subsequent global fusion decision. The FC fuses the received local results and makes the decision about the authorized spectrum according to the k-out-of-n rule [31]. The final decision F(k) can be written as:…”

Section: Methodology a System Modelmentioning

confidence: 99%

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Luo

2020

IEEE Access

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Cooperative spectrum sensing can be regarded as a promising method to resolve the spectrum scarcity owing to achieving spatial diversity gain in cognitive radio sensor networks. However, the spectrum sensing data falsification attack launched by the malicious nodes will result in the wrong decision in the fusion center owing to the falsified observations. It will cause a serious security threat and degrade the decision making process. In this paper, we propose a secure cooperative spectrum sensing strategy based on reputation mechanism for cognitive wireless sensor networks to counter above kind of attack. The beta reputation model is applied to assign reputation value to cognitive sensor nodes according to their historical sensing behavior, and a dynamic trust evaluation scheme of cooperative spectrum sensing is established. In the final decision, the fusion center allocates a reasonable weight value according to the evaluation of the submitted observations to improve the accuracy of the sensing results. Simulation results support that our proposed strategy can weaken the impact of sensing data falsification attacks in cooperative sensing and outperform some traditional methods. INDEX TERMS Cooperative spectrum sensing, reputation mechanism, cognitive wireless sensor networks, cognitive radio.

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Section: Methodology a System Modelmentioning

confidence: 99%

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Luo

2020

IEEE Access

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“…It can use spatial diversity gain to improve the correlation of sensing information, and solve the problems of multipath fading, shadow fading and hidden terminal. To improve the energy efficiency of CSS, Peng et al [12] introduced a optimal cooperative nodes selection scheme with modulation constellation size. To alleviate the environmental interference, Manish et al [13] evaluated the cooperative SU's reliability according to the historical decisions and proposed an optimal weight assignment mechanism for CSS.…”

Section: Related Workmentioning

confidence: 99%

Optimal Linear Weighted Cooperative Spectrum Sensing for Clustered-based Cognitive Radio Networks

Yé

Jiang

2021

Preprint

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The lack of spectrum resources restricts the development of the wireless communication-oriented applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, cognitive radio is regarded as an effective technology. Cooperative spectrum sensing with multi cognitive users can improve the low detection performance caused by channel fading or shadow effect. However, it also may lead to poor detection accuracy due to poor channel conditions of individual users. In order to solve the above problems, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

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“…In addition, when the input SNR is too low, the processing center can appropriately increase the number of sensing nodes to improve the recovery performance. However, the method for selecting an optimal sensing node as the sampling channel for the DMWC is worthy of consideration , .…”

Section: Background and Motivationmentioning

confidence: 99%

Broadband Spectrum Sensing of Distributed Modulated Wideband Converter Based on Markov Random Field

Li¹,

Zhu²,

Zhou³

et al. 2018

ETRI Journal

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The Distributed Modulated Wideband Converter (DMWC) is a networking system developed from the Modulated Wideband Converter, which converts all sampling channels into sensing nodes with number variables to implement signal undersampling. When the number of sparse subbands changes, the number of nodes can be adjusted flexibly to improve the reconstruction rate. Owing to the different attenuations of distributed nodes in different locations, it is worthwhile to find out how to select the optimal sensing node as the sampling channel. This paper proposes the spectrum sensing of DMWC based on a Markov random field (MRF) to select the ideal node, which is compared to the image edge segmentation. The attenuation of the candidate nodes is estimated based on the attenuation of the neighboring nodes that have participated in the DMWC system. Theoretical analysis and numerical simulations show that neighboring attenuation plays an important role in determining the node selection, and selecting the node using MRF can avoid serious transmission attenuation. Furthermore, DMWC can greatly improve recovery performance by using a Markov random field compared with random selection.

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Joint Selection for Cooperative Spectrum Sensing in Wireless Sensor Networks

Cited by 15 publications

References 5 publications

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Optimal Linear Weighted Cooperative Spectrum Sensing for Clustered-based Cognitive Radio Networks

Broadband Spectrum Sensing of Distributed Modulated Wideband Converter Based on Markov Random Field

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