Survey and taxonomy of clustering algorithms in 5G

Khan, Muhammad Fahad; Yau, Kok‐Lim Alvin; Noor, Rafidah Md; Imran, Muhammad Ali

doi:10.1016/j.jnca.2020.102539

Cited by 27 publications

(24 citation statements)

References 192 publications

(283 reference statements)

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“…Nowadays, clustering has been introduced as one of the important factors in upcoming new wireless generations as well [35]. A clustering-based reliable low-latency routing scheme using the ACO method for vehicular networks [36] is used to enhance VANETs QoS. In this algorithm, ACO is used with two different schemes-AODV and clustering-based reliable low-latency multipath routing (CRLLR) scheme, where CRLLR is efficient in latency and number of beacons messages on the cost of more energy consumption and minimum reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

et al. 2020

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In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.

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Section: Literature Reviewmentioning

confidence: 99%

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

et al. 2020

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“…However, to accommodate such services, future networks must go beyond the capabilities of their current generation counterparts [ 1 , 2 ]. Cellular users are actively engaged in generating and sharing the content of various types [ 3 ]. Therefore, future networks will require to handle significantly higher multimedia services [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, future networks will require to handle significantly higher multimedia services [ 4 , 5 ]. Existing centralized architectures and mechanisms may not be able to meet the content-sharing demands [ 2 , 3 ]. Decentralized mechanism and load mitigation in cellular networks are required to meet the rising demand of content sharing.…”

Section: Introductionmentioning

confidence: 99%

“…Various architectural frameworks have been proposed in the literature to support D2D-based content sharing. The concept of clustering users in proximity sharing a common interest has been very popular for multicasting scenarios [ 3 ]. Typically, an intermediate node, termed as cluster head (CH) fetches the content from the Base Station (BS) and delivers it to several content requestors [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…We presented the effectiveness of utilizing clustering in a D2D multicasting scenario in one of our previous works [ 13 ]. Formation of appropriate clusters is significant as it influences the performance of the underlay cellular networks [ 3 ]. Considering all these, we propose a multifactor weighted clustering algorithm in this article.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks

Aslam

Alam

Hasan

et al. 2020

Sensors

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Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.

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An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

Mughal,

He,

Zhu

et al. 2023

Trans Emerging Tel Tech

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Recently, heterogeneous cluster networks (HCNs) have been the subject of significant research. The nature of the next‐generation HCN environment is decentralized and highly dynamic; optimization techniques cannot quite express the dynamic characteristics of node resource utilization and communication of HCN networks. In this article, we present an intelligent Hybrid‐Q Learning approach (Hybrid QL)‐based clustering approach for IoT and WSN. Using the self‐learning abilities of (HCNs), we propose a model for dynamic accessing systems on nodes and agents that identify the best possible paths and communication over heterogeneous cluster networks using reinforcement learning. In addition to reducing energy consumption, it creates efficient and effective resource utilization and node communication performance. Through increased throughput and link management, the HCN aims to reduce energy consumption. The proposed model is compared to existing approaches based on various scenarios. Finally, the results of the evaluation tasks demonstrate high accuracy, low‐level complexity, fast dynamic response times, and scalability for heterogeneous cluster networks. Our model showed exceptional node allocation efficiency for dynamic IOT environments and WSNs.

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Survey and taxonomy of clustering algorithms in 5G

Cited by 27 publications

References 192 publications

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks

An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

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