A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Ling, Mee Hong; Yau, Kok‐Lim Alvin; Qadir, Junaid; Ni, Qiang

doi:10.1109/tccn.2018.2881135

Cited by 26 publications

(13 citation statements)

References 35 publications

(36 reference statements)

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“…Smart malicious SUs can attack the cluster heads to mislead them into inappropriately adjusting the cluster size. To defend against this attack, an RL-based trust model was proposed in [30] to improve traditional budget-based cluster size adjustment schemes. The proposed method enables the cluster head to observe and learn about the behaviors of its SU member nodes and to revoke the membership of malicious SUs in order to alleviate the effects of collaborative intelligent attacks while adjusting the cluster size dynamically according to the availability of white spaces.…”

Section: A ML Based Crnmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

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Section: A ML Based Crnmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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“…In [26], the authors proposed a section-based cluster mechanism, which clustered vehicles based on road sections and selected the vehicle closest to the cluster center as the CH, ignoring the driving stability of vehicles. In [27], the authors proposed a dynamic cluster adjustment mechanism to improve the scalability of the vehicular network, where CH discovered malicious cluster member (CM) nodes and adjusted cluster size according to the available spectrum to maximize resource utilization. The authors in [28] proposed a recommendation and switching mechanism in heterogeneous IoV to alleviate the load on the cellular network and improve network performance.…”

Section: Network Clustering and Switching In Iovmentioning

confidence: 99%

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Chang

Ning

2021

Sensors

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Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.

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“…As such leveraging the power of artificial intelligence [103], [104] may revolutionize all aspect of security which has become increasingly complex. Nevertheless, the use of artificial intelligence can cause security vulnerabilities as attackers can manipulate learned knowledge, and so proper rules should be designed and imposed [105].…”

Section: H Cyber Security Of Smart Portsmentioning

confidence: 99%

Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology

et al. 2020

Self Cite

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In this digital age, ports face stiff competition in global supply chain. Smart ports, as high performing ports, utilize information and communications technology (ICT) to provide a wide range of smart applications, resulting in vastly improved vessels and container management among others, which subsequently improve the competitiveness and sustainability of the national economy. While various novel solutions, such as information system and locating system, have been proposed to improve smart port activities, there are several key issues pertaining to ports and port operations that warrant specific attention, particularly greenhouse gases emission, which has accelerated to an alarming level. The urgent need to address such issues is lacking. This article aims to offer a review of the research literature on smart ports, including Internet of Things platform, greenhouse gases emission reduction, energy efficiency enhancement, and so on. The objective is to establish a foundation of existing research conducted on smart ports in order to motivate new research interests in this area. Open issues are also presented to foster new research initiatives on smart ports.

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A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Cited by 26 publications

References 35 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology

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