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

Aslam, Saad; Alam, Fakhrul; Hasan, Syed Faraz; Rashid, M. A.

doi:10.3390/s20195509

Cited by 12 publications

(9 citation statements)

References 69 publications

(115 reference statements)

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“…A distributed architecture based on content-centric networking and network virtualization was proposed in [31] to enable efficient content delivery. Clustering was introduced at the user level for content distribution, and a weighted multifactor clustering approach was presented for grouping D2D User Equipment (DUEs) with a common interest.…”

Section: Related Workmentioning

confidence: 99%

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

Khan

Arshed

et al. 2022

Security and Communication Networks

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In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares content equally across devices based on their capacities and battery life under mobility. The proposed work includes cluster formation, cluster head selection, and helper node selection. In addition, we relied on a cooperative caching policy to ensure that content is distributed efficiently. The simulation results indicate a 12.05% reduction in energy compared to the state-of-the-art technique with a 2-gigabyte video file. To evaluate scalability, we increased the file size from 3 to 4 gigabytes, yet the performance in terms of energy consumption remained the same.

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Section: Related Workmentioning

confidence: 99%

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

Khan

Arshed

et al. 2022

Security and Communication Networks

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“…The details of such relevant process can be found in the clustering literature (e.g. see [25], [43] that shows network latency is not impacted significantly.…”

Section: Proposed Machine Learning Approachmentioning

confidence: 99%

“…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%

“…Clustering in a multicasting scenario intends to improve performance by reducing energy consumption [18]- [24] and increasing throughput [25]- [28]. These works show that the importance of clustering is not specific to content-centric broadcast networks and can be applied to various applications as shown in the literature [29]- [43]. Table 1 summarizes various studies focusing on clustering.…”

Section: Introductionmentioning

confidence: 99%

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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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“…However, the issue of picking the cluster head in the model of cluster in multi‐hop is more complex when compared to one‐hop model. The grounds that are present in a multihop model necessitate that the cluster head accept an additional hub as a parent hub for interfacing with the base station 11 . Though in a solitary bounce model, it is time to decide the cluster head of each gathering that will discuss straightforwardly with the base station.…”

Section: Introductionmentioning

confidence: 99%

Analysis of dynamic topology wireless sensor networks for the internet of things (IoT)

R¹,

Raj²,

Smys³

2021

Int J Communication

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Summary The availability of the smart existing systems utilizes the system assets for crucial internet of things (IoT). The IoT visions to develop effectively with the processing model. Past customary versatile figuring set‐ups utilize advanced mobile phones and portables. A sensor could drop out for various reasons, for the system to act naturally adjusting and consider multipath steering. Dynamic topology for a remote sensor arranges all the detecting hubs that are associated together for gathering information that are transmitted. The goals that are accompanying are set for exploring the work that are to be finished. Several sensor hubs in the system are arranged for the improvement of proficiency dynamic topology. The novel strategy has been proposed for the improvement in oneself sorting out, multi‐bounce systems for the remote sensor hubs. Because of its ability to decrease energy utilisation, a cluster‐based model is the best in the remote sensor arrangement. The cluster head selection processes are a lumbering procedure that influences execution on the system execution. Few investigations propose the determination of the cluster head techniques that are being large portion of them that are not fitting for a powerful grouping condition. The calculations for the proposed methodology work better inside a solitary bounce grouping model structure, and the system lifetime establishes a major issue if there should be an occurrence of multi‐jump bunching conditions. The manuscript presents cluster head determination technique novel single‐hop and multi‐hop cluster head selection by GA (S/M HCH‐GA) in wireless sensor networks (WSNs) for both one‐hop as well as multi‐hop cluster models. The technique that is proposed intends to meet the prerequisites of dynamic situations; this is done by choosing cluster head that is dependent on the energy that remains along with the energy that is consumed.

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

Cited by 12 publications

References 69 publications

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

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

Analysis of dynamic topology wireless sensor networks for the internet of things (IoT)

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