SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks

Saleem, Yasir; Yau, Kok‐Lim Alvin; Mohamad, Hafizal; Ramli, Nordin; Rehmani, Mubashir Husain

doi:10.1016/j.comnet.2015.08.019

Cited by 43 publications

(16 citation statements)

References 33 publications

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“…Nowadays, scholars have proposed many routing protocols for CRNs. To address the routing problem in CRNs caused by dynamic channel availability, Saleem et al [2][3][4] presented a cluster-based routing scheme, called SMART, which consisted of clustering mechanisms and an artificial intelligence approach. Zareei et al [5] proposed a novel ondemand cluster-based hybrid routing protocol for CRAHNs.…”

Section: Related Workmentioning

confidence: 99%

“…The details of the Load Balancing Relay Selection Algorithm are shown in Algorithm 2. In Algorithm 2, lines (1) and (2)…”

Section: Load Balancing Based Relay Selection Algorithmmentioning

confidence: 99%

“…In recent years, several routing protocols [2][3][4][5][6][7][8] have been proposed for CRAHNs. However, most of these studies focus on how to design a proper metric to measure the quality of a preselected route from the source to the destination.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Load Balancing Opportunistic Routing for Cognitive Radio Ad Hoc Networks

Duan

Tang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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Recent research activities have shown that opportunistic routing can achieve considerable performance gains in Cognitive Radio Ad hoc Networks (CRAHNs). Most of these studies focused on designing appropriate metrics to select and prioritize the forwarding candidates. However, in multiple-flow networks, a small number of nodes may always be with the higher priority order for different flows. Thus, some nodes may easily become overloaded with too much traffic and be severely congested. To overcome this problem, we propose a load balancing opportunistic routing (LBOR) scheme to maximize the total throughput of the whole network. We first formulate the problem of maximizing the total throughput of the network as a linear programming problem. Then, we develop heuristic load balancing candidate forwarder sorting and selection algorithms. Simulation results and comparisons demonstrate that our proposed LBOR scheme outperforms existing opportunistic routing protocols with nonload balancing methods in CRAHNs.

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Section: Related Workmentioning

confidence: 99%

“…The details of the Load Balancing Relay Selection Algorithm are shown in Algorithm 2. In Algorithm 2, lines (1) and (2)…”

Section: Load Balancing Based Relay Selection Algorithmmentioning

confidence: 99%

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Load Balancing Opportunistic Routing for Cognitive Radio Ad Hoc Networks

Duan

Tang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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“…If a PCH does not receive any cluster head message within a threshold time then it declares itself as a cluster head and informs the neighbors to join the cluster via broadcasting cluster head request messages. Saleem et al [18] presented a novel clustering algorithm that utilizes the channel quality metric for cluster formation. The channel quality metric of a channel is measured in terms of the likelihood of the channel to be in the off state at a specified time.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectrum aware clustering in cognitive radio ad hoc networks

Ukey¹,

Chawla²

2018

IJET

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Cognitive radio (CR) is an emerging technology developed for efficient utilization of the radio spectrum. CRN utilizes CR technology and enables the unlicensed users also referred as secondary users (SUs) to access free portions of the licensed spectrum in an opportunistic manner. To support scalability and stability in distributed CRNs also referred as cognitive radio ad hoc networks (CRAHNs), SUs are often organized into smaller groups known as clusters. Spectrum aware clustering is considered as the key technique to overcome numerous is-sues associated with the dynamic nature of CRAHNs. This article focuses on clustering in CRAHNs and presents a comprehensive review of various spectrum aware clustering algorithms presented in the literature. The article highlights notable clustering metrics and includes the description of cluster formation and maintenance process. The article also renders potential research gaps in existing research works and discusses open challenges and issues that need to be addressed for efficient clustering in CRAHNs.

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“…Larger cluster size improves network scalability, and it offers two main advantages. Firstly, the overhead reduces since: a) routing messages are exchanged among nodes in a backbone route only, and b) clustering messages for updating the network changes (e.g., the demand for white spaces) are exchanged at the cluster level only rather than the network level [4]. The problem is that malicious nodes can launch random or intelligent attacks against cluster size, impeding the goals of clustering to achieve the two main advantages.…”

Section: Introductionmentioning

confidence: 99%

A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

et al. 2019

Self Cite

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Cognitive radio network (CRN) is the next generation wireless network that allows unlicensed users [secondary users (SUs)] to explore and use the underutilised licensed channels (white spaces) owned by licensed users (primary users). The purpose is to increase the spectrum utilization for enhanced network performance. Clustering segregates SUs in a CRN into logical groups (clusters) with each consisting of a leader (cluster head) and member nodes. A budget-based cluster size adjustment scheme is applied to enable each cluster to adjust its number of member nodes in its cluster based on the availability of white spaces in order to improve network scalability. However, cluster size adjustment is prone to attacks by malicious SUs that launch random and intelligent attacks. Hence, we incorporate an artificial intelligence approach called reinforcement learning (RL) into a trust model to countermeasure the random and intelligent attacks. The simulation results show that RL-based trust model increases the utilization of white spaces and cluster size to improve network scalability and enhance network performance despite the presence of RL-based intelligent attacks. INDEX TERMS Artificial intelligence, reinforcement learning, attacks, trust model, cognitive radio.

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SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks

Cited by 43 publications

References 33 publications

Load Balancing Opportunistic Routing for Cognitive Radio Ad Hoc Networks

Load Balancing Opportunistic Routing for Cognitive Radio Ad Hoc Networks

Spectrum aware clustering in cognitive radio ad hoc networks

A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

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