Research on an Evaluation Algorithm of Sensing Node Reliability in Cognitive Networks

Huang, Tangsen; Li, Xiaowu; Cao, Qi

doi:10.1109/access.2020.2965292

Cited by 4 publications

(4 citation statements)

References 24 publications

(32 reference statements)

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“…e i,j represents the energy consumption of the j-th node in the i-th cycle. The value of comprehensive correction coefficient ν is calculated by formula (11).…”

Section: Node Estimation and Selection Algorithmmentioning

confidence: 99%

“…ν j = 0.4ρ j + 0.6ξ j (11) ν j represents the comprehensive correction coefficient of the j-th node, which is obtained by the weighted sum of the performance correction coefficient and energy consumption correction coefficient. The performance correction coefficient accounts for 40% and the energy consumption correction coefficient accounts for 60%.…”

Section: Node Estimation and Selection Algorithmmentioning

confidence: 99%

“…In order to extend the working life of cognitive wireless networks, many improved methods are used,the method of switching on and off time is adopted to extend the working life of the cognitive network [11], but this method has low perception efficiency and misses many opportunities for spectrum access. The method of cooperating between nodes and the primary user according to the agreement is used to reduce the workload of the cognitive network [12].…”

Section: Introductionmentioning

confidence: 99%

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A Blockchain-Based Node Selection Algorithm in Cognitive Wireless Networks

Huang

Ying

2020

IEEE Access

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In cognitive wireless networks, multi-node cooperative spectrum sensing can effectively improve the accuracy of spectrum sensing, but there is a non-linear relationship between the number of nodes and sensing accuracy. Nodes with low reliability participate in cooperative sensing, which is not conducive to the improvement of sensing accuracy, and reduces the energy efficiency of spectrum sensing, which poses challenges to the normal operation of cognitive wireless networks. In order to improve energy efficiency and sensing performance, this paper proposes the node evaluation and scheduling (NES) algorithm and the Secure Spectrum Sensing based on Blockchain (SSSB) algorithm, which can evaluate the reliability of sensing nodes in real time, and obtain the trust value of the node. The nodes information is stored in the management center of blockchain. Blockchain encrypts nodes information to ensure that a node corresponds to its own trust value without confusion. Fusion Center of cognitive wireless networks select good performance nodes to participate in cooperative spectrum sensing. Which can reduce energy consumption while improving the sensing performance. The simulation experiment results show that the new algorithm in this paper is far superior to the traditional algorithm. Under the same other conditions, the detection probability is increased by 5%, and the energy consumption is reduced by 10%, and the safety index has also been greatly improved.INDEX TERMS Cognitive wireless networks, blockchain, energy efficiency, sensing accuracy.

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“…e i,j represents the energy consumption of the j-th node in the i-th cycle. The value of comprehensive correction coefficient ν is calculated by formula (11).…”

Section: Node Estimation and Selection Algorithmmentioning

confidence: 99%

Section: Node Estimation and Selection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Blockchain-Based Node Selection Algorithm in Cognitive Wireless Networks

Huang

Ying

2020

IEEE Access

Self Cite

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“…As a result, in cognitive wireless networks, multi-node CSS is a widely utilized sensing technique that can successfully enhance perception reliability, but as the number of sensor nodes grows, so does the energy utilization of the cognitive wireless network, and because the front end of the cognitive network is a rechargeable battery, the energy utilization grows as well. Many enhanced ways are used to increase the lifetime of cognitive wireless networks 3 . The technique of flipping on and off time is being used to prolong the lifetime of the cognitive network and hence reduces the perception efficiency which is ineffective for the spectrum access.…”

Section: Introductionmentioning

confidence: 99%

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities

Rani

Babbar

Shah

et al. 2022

Sci Rep

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In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of $$\alpha = 1.5$$ α = 1.5 and $$\gamma = 2.5$$ γ = 2.5 with the varying number of nodes.

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Dynamic Node Management for Energy Optimization in Cognitive Radio Systems

Huang,

Yin,

Yin

et al. 2024

Journal of Electrical and Computer Engineering

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Energy efficiency and spectrum sensing performance are two crucial parameters in cognitive radio networks. Enhancing energy efficiency should not come at the cost of sacrificing spectrum sensing performance. This paper introduces a novel sensing node grouping algorithm to address the challenge of balancing energy efficiency and sensing performance. The proposed algorithm first prioritizes spectrum sensing training for each node and estimates their reliability. Nodes with reliability values exceeding a predefined threshold are selected for cooperative spectrum sensing. The selected nodes are divided into two groups using a staggered grouping approach, ensuring equivalent sensing performance between the groups. The groups alternate in performing spectrum sensing operations, with one group engaging in sensing while the other participates in data transmission or remains idle. The optimal number of cooperative nodes is determined through a greedy algorithm, formulated as a constrained optimization problem. The objective is to maximize energy efficiency subject to a constraint on the error probability, ensuring the highest possible energy efficiency while satisfying the required sensing performance criteria. Experimental results demonstrate that the proposed algorithm effectively maximizes energy efficiency while fulfilling the sensing performance requirements. Compared to traditional algorithms, the dynamic grouping algorithm significantly enhances spectrum sensing performance and improves energy efficiency. The algorithm is adaptable to various network configurations and environmental conditions, addressing the challenges faced by existing solutions. This research contributes to the advancement of cognitive radio networks and their applications in various domains, offering a comprehensive and adaptive solution to optimize energy efficiency and spectrum sensing performance.

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Research on an Evaluation Algorithm of Sensing Node Reliability in Cognitive Networks

Cited by 4 publications

References 24 publications

A Blockchain-Based Node Selection Algorithm in Cognitive Wireless Networks

A Blockchain-Based Node Selection Algorithm in Cognitive Wireless Networks

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities

Dynamic Node Management for Energy Optimization in Cognitive Radio Systems

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