A Sub-spectrum Sensing Scheme Based on Reputation in Cognitive Radio Networks

Li, Hongning; Pei, Qingqi; Jiang, Xiaohong; Liang, Rui; Geng, Peng

doi:10.1109/cis.2010.110

Cited by 8 publications

(9 citation statements)

References 3 publications

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“…Denote the final decision by D ( t ) = {0,1}, where D ( t ) = 0 and D ( t ) = 1 indicate the absence and presence of PUs, respectively. A general equation to calculate reputation value is

r_{i} (t) = r_{i} (t - 1) + {((), - 1)}^{d_{i} (t) + D (t)}

, where at time t , d i ( t ) is the sensing outcome of SU i and D ( t ) is the final decision .…”

Section: Trust and Reputation Management Performance And Complexity Amentioning

confidence: 99%

“…For a more scalable TRM, the structure can be decentralized further by employing clusterheads to help disseminate the reputation information . A clusterhead that is similar to can also act as a: (1) distributed information repository; (2) global reputation values decision maker; (3) SUs' feedback storage. Similar to Li et al .…”

Section: Open Issuesmentioning

confidence: 99%

“…As an example, Li et al . propose a TRM scheme called sub‐spectrum sensing scheme (SSSS) for collaborative spectrum sensing schemes in hierarchical or clustered networks. In SSSS, each clusterhead makes final decisions on the sensing outcomes and sends them to a decision fusion center.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trust and reputation management in cognitive radio networks: a survey

Ling

Yau

Poh

2013

Security Comm Networks

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Cognitive radio (CR), which is the next generation wireless communication system, enables unlicensed users or secondary users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or primary users (PUs) so that bandwidth availability improves at the SUs, which helps to improve overall spectrum utilization. Collaboration is an intrinsic characteristic of CR to improve network performance. For instance, in collaborative spectrum sensing, SU hosts generate sensing outcomes, and collaborate amongst themselves through making final decisions at a decision fusion center in order to improve the accuracy of spectrum sensing. The requirement to collaborate has inevitably opened doors to various forms of attacks by malicious SUs, and this critical issue can be addressed using trust and reputation management (TRM), and so this is the focus of this article. Generally speaking, TRM detects malicious SUs, including honest SUs that turn malicious. Hence, TRM is of paramount importance in most kinds of schemes that require collaboration in CR networks. Our contribution in this article is as follows. This article provides an extensive survey on the application of TRM in various schemes in CR networks in order to ameliorate the effects of malicious SUs in collaboration. The discussion is presented with respect to a TRM taxonomy, various approaches to achieve TRM, various attack models, as well as the challenges and characteristics associated with TRM. Because of the significance of TRM in collaboration, this article presents a wide range of open issues to warrant further research in this area. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Denote the final decision by D ( t ) = {0,1}, where D ( t ) = 0 and D ( t ) = 1 indicate the absence and presence of PUs, respectively. A general equation to calculate reputation value is

r_{i} (t) = r_{i} (t - 1) + {((), - 1)}^{d_{i} (t) + D (t)}

, where at time t , d i ( t ) is the sensing outcome of SU i and D ( t ) is the final decision .…”

Section: Trust and Reputation Management Performance And Complexity Amentioning

confidence: 99%

Section: Open Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Trust and reputation management in cognitive radio networks: a survey

Ling

Yau

Poh

2013

Security Comm Networks

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“…FADS has some significant advantages over traditional sensors [3,4], including the higher measurement accuracy and lighter equipment weight. Also, the overall system is installed inside the body such that the vehicle configuration can adapt to the complicated flight environment with consideration of the large angle of attack and high flight dynamic pressure.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on a Solving Model and Algorithm for a Flush Air Data Sensing System

Liu

Xiao

2014

Sensors

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With the development of high-performance aircraft, precise air data are necessary to complete challenging tasks such as flight maneuvering with large angles of attack and high speed. As a result, the flush air data sensing system (FADS) was developed to satisfy the stricter control demands. In this paper, comparative stuides on the solving model and algorithm for FADS are conducted. First, the basic principles of FADS are given to elucidate the nonlinear relations between the inputs and the outputs. Then, several different solving models and algorithms of FADS are provided to compute the air data, including the angle of attck, sideslip angle, dynamic pressure and static pressure. Afterwards, the evaluation criteria of the resulting models and algorithms are discussed to satisfy the real design demands. Futhermore, a simulation using these algorithms is performed to identify the properites of the distinct models and algorithms such as the measuring precision and real-time features. The advantages of these models and algorithms corresponding to the different flight conditions are also analyzed, furthermore, some suggestions on their engineering applications are proposed to help future research.

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“…where is the sensing outcome of SU i and is the final decision given by a fusion center [16]. In our spectrum leasing model, increases when SU i has successfully relay the packets for PU, where is the expected packet relay destination of SU i and is the packet relay destination requested by PU.…”

mentioning

confidence: 99%

Reinforcement Learning-Based Trust and Reputation Model for Spectrum Leasing in Cognitive Radio Networks

Ling

Yau

2013

2013 International Conference on IT Convergence and Security (ICITCS)

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Abstract-Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users (PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field.

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A Sub-spectrum Sensing Scheme Based on Reputation in Cognitive Radio Networks

Cited by 8 publications

References 3 publications

Trust and reputation management in cognitive radio networks: a survey

Trust and reputation management in cognitive radio networks: a survey

Comparative Study on a Solving Model and Algorithm for a Flush Air Data Sensing System

Reinforcement Learning-Based Trust and Reputation Model for Spectrum Leasing in Cognitive Radio Networks

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