Cluster head selection method for content‐centric mobile social network in 5G

Tulu, Muluneh Mekonnen; Hou, Ronghui; Li, Chuanyin; Amentie, Melkamu Deressa

doi:10.1049/iet-com.2016.1433

Cited by 16 publications

(19 citation statements)

References 25 publications

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“…The remaining two are the state-of-the-art and recently proposed algorithms. “Benchmarked I” has been proposed by Tulu.M.M et al [ 69 ]. This algorithm applies the concept of entropy of betweenness centrality (EBC) to select CHs for content-sharing.…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

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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“…Two of the benchmarked clustering algorithms represent state-of-theart whereas the other two represent the classical clustering techniques. The classical algorithms, K-Medoids and Density-Based Spatial Clustering of Applications With Noise (DBSCAN) [12] can be widely found in the literature whereas the work presented in [31] and [43] are recently proposed algorithms, in this document, we termed them as ''EBC'' and ''Multi-Factor'' respectively. EBC algorithm selects CHs based on the entropy of betweenness centrality, which considers social-interest and shortest path between the users.…”

Section: F Analysis Of the Performance Parametersmentioning

confidence: 99%

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

et al. 2021

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Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters. INDEX TERMSClustering algorithm, content multicasting, D2D enabled networks, deep neural networks, eNB loading, machine learning, random forest, support vector machine, user segregation.

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“…The work in Tulu et al (2018) resorts to spatial network metrics to calculate influence nodes to serve as contentcentric mobile 5G networks. However, again, they focus their experiments on small network graphs (85 nodes).…”

Section: Related Workmentioning

confidence: 99%

Advanced spatial network metrics for cognitive management of 5G networks

et al. 2020

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The emerging fifth-generation (5G) mobile networks are empowered by softwarization and programmability, leading to the huge potentials of unprecedented flexibility and capability in cognitive network management such as self-reconfiguration and self-optimization. To help unlock such potentials, this paper proposes a novel framework that is able to monitor and calculate 5G network topological information in terms of advanced spatial metrics. These metrics, together with enabling and optimization algorithms, are purposely designed to address the complexity of 5G network topologies introduced by network virtualization and infrastructure sharing among operators (multi-tenancy). Consequently, this new framework, centred on a topology monitoring agent (TMA), enables on-demand 5G networks' spatial knowledge and topological awareness required by 5G cognitive network management in making smart decisions in various autonomous network management tasks including but not limited to virtual network function placement strategies. The paper describes several technical use cases enabled by the proposed framework, including proactive cache allocation, computation offloading, node overloading alerting, and load balancing. Finally, a realistic 5G testbed is deployed with the central component TMA, together with the new spatial metrics and associated algorithms, implemented. Experimental results empirically validate the proposed approach and demonstrate the scalability and performance of the TMA component. Keywords 5G networks • Topology management • Spatial network metrics • Cognitive management 1 Introduction Network management in the forthcoming fifth-generation (5G) mobile networks is notably influenced by the softwarization of network infrastructures where several hardware components are virtualized and by the multi-tenancy of the network infrastructures where hardware components are shared by different mobile operators. The main motivation of these 5G capabilities is the reduction of both capital and operational costs. 5G virtual network functions (VNFs) can now be deployed automatically and on-demand on the Edge and the Core segments of the 5G network and can be migrated between the computers that belong to the same net-Communicated by V. Loia.

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Cluster head selection method for content‐centric mobile social network in 5G

Cited by 16 publications

References 25 publications

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

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

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

Advanced spatial network metrics for cognitive management of 5G networks

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