RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks

Javed, Zunera; Yau, Kok‐Lim Alvin; Mohamad, Hafizal; Ramli, Nordin; Qadir, Junaid; Ni, Qiang

doi:10.1109/access.2017.2777867

Cited by 15 publications

(14 citation statements)

References 27 publications

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“…RL-budget -In clustered networks, cluster heads close to the sink may require a higher energy consumption than cluster heads on the border. To balance energy consumption between cluster heads, [91] proposed a protocol to adjust the size (in number of members) of clusters in radio cognitive networks. Periodically, a cluster head broadcasts a message including the available channels (representing the budget) to invite nodes to join its cluster.…”

Section: R-crs (Natg) (Rl-based Cooperative Relay Selection) -mentioning

confidence: 99%

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Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Mammeri¹

2019

IEEE Access

180

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Reinforcement learning (RL), which is a class of machine learning, provides a framework by which a system can learn from its previous interactions with its environment to efficiently select its actions in the future. RL has been used in a number of application fields, including game playing, robotics and control, networks, and telecommunications, for building autonomous systems that improve themselves with experience. It is commonly accepted that RL is suitable for solving optimization problems related to distributed systems in general and to routing in networks in particular. RL also has reasonable overheadin terms of control packets, memory and computation-compared to other optimization techniques used to solve the same problems. Since the mid-1990s, over 60 protocols have been proposed, with major or minor contributions in the field of optimal route selection to convey packets in different types of communication networks under various user QoS requirements. This paper provides a comprehensive review of the literature on the topic. The review is structured in a way that shows how network characteristics and requirements were gradually considered over time. Classification criteria are proposed to present and qualitatively compare existing RL-based routing protocols.INDEX TERMS Reinforcement learning, communication networks, routing protocols, path optimization, quality of service.

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Section: R-crs (Natg) (Rl-based Cooperative Relay Selection) -mentioning

confidence: 99%

“…Depending on its available resources, a cluster head determines the number of members that can join the cluster. Then, following the transmission of data packets from members, cluster heads receive feedback and adjusts (i.e., optimizes) their cluster size accordingly [82], [91].…”

Section: ) Routing Optimization Contextmentioning

confidence: 99%

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Mammeri¹

2019

IEEE Access

180

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“…In order to improve network scalability and cluster stability in CRNs, the authors in Reference 22 proposed a scheme that adapted the cluster size with the amount of white space as time goes.…”

Section: Clustering In Crnsmentioning

confidence: 99%

A new fast k‐nearest neighbor classification algorithm in cognitive radio networks based on parallel computing

Benmammar

2020

Concurrency and Computation

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Summary The field of telecommunication has undergone a very rapid technological evolution, which has forced researchers to find techniques that allow better exploitation of hardware and software. Among the proposed technologies, cognitive radio, a concept that was designed after several technologies such as software radio. Cognitive radio has been widely used for opportunistic access of the shared spectrum and has defined the cognitive nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. In this context, clustering techniques were adopted in cognitive radio networks (CRNs) due to their great advantages especially for routing. In this article, we propose a parallel mode of the k‐NN algorithm. The aim is to make a fast assignment of radio nodes in CRNs organized in the form of clusters. The obtained results are very satisfactory because we have been able to reduce to about 50% the execution time of the basic algorithm (sequential).

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“…While the attackers launch intelligent attacks to create a more detrimental effect to the clusterhead, the clusterhead also counters the attack by leveraging on RL-based trust model for cluster size adjustment to provide cluster scalability. In [24], an in-depth study was done on cluster size adjustment in the absence of attackers. While some research has been done on cluster size adjustment to improve network scalability, to the best of our knowledge, the study on the effects of intelligent attacks, which are detrimental to network scalability and to the clusterhead has not been carried out; and this is the primary motivation for our article.…”

Section: Motivation and Significancementioning

confidence: 99%

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

Ling

Yau

Qadir

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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Cognitive radio enables secondary users (SUs) to explore and exploit the underutilized licensed channels (or white spaces) owned by the primary users. To improve the network scalability, the SUs are organized into clusters. This article proposes a novel artificial intelligence based trust model approach that uses reinforcement learning (RL) to improve traditional budget-based cluster size adjustment schemes. The RL-based trust model enables the clusterhead to observe and learn about the behaviors of its SU member nodes, and revoke the membership of malicious SUs in order to ameliorate the effects of intelligent and collaborative attacks, while adjusting the cluster size dynamically according to the availability of white spaces. The malicious SUs launch attacks on clusterheads causing the cluster size to become inappropriately sized while learning to remain undetected. In any attack and defense scenario, both the attackers and the clusterhead adopt RL approaches. Simulation results have shown that the single-agent RL (SARL) attackers have caused the cluster size to reduce significantly; while the SARL clusterhead has slightly helped increase its cluster size, and this motivates a rule-based approach to efficiently counterattack. Multi-agent RL attacks have shown to be less effective in an operating environment that is dynamic.Index Terms-Cognitive radio ad hoc networks, clustering methods, trust management, artificial intelligence, security.

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RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks

Cited by 15 publications

References 27 publications

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

A new fast k‐nearest neighbor classification algorithm in cognitive radio networks based on parallel computing

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

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