Relay selection based clustering techniques for high density LTE networks

Hajjar, Maryam; Aldabbagh, Ghadah; Dimitriou, Nikos; Win, Moe Z.

doi:10.1007/s11276-018-1658-7

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…These cluster heads transmit data of all other users in its cluster. The proposed algorithm improves the system capacity and energy consumption compared to other similar work [27].…”

Section: A Related Workmentioning

confidence: 84%

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Novel Relay Selection Algorithms for Machine-to-Machine Communications With Static RF Interfaces Setting

et al. 2020

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Machine-to-Machine (M2M) communications play a significant role in the Internet of Things (IoT). Cooperation of machines in M2M communications can improve network performance when the quality of connections between sources and their destinations is poor. Careful selection of machines as relays can play an effective role in improving the quality of communication in dense networks. Furthermore, the possibility of simultaneous use of different Radio Frequency (RF) interfaces can increase the capacity of data transmission over the network. In this paper, two novel M2M relay selection algorithms are proposed namely Optimal Relay Selection Algorithm (ORSA) and Matching based Relay Selection Algorithm (MRSA). ORSA is a centralized algorithm that transforms the relay selection problem to a kcardinality assignment problem that can be solved using the Hungarian algorithm. MRSA is a decentralized algorithm that leverages concepts from matching theory to provide a stable relay assignment. In both proposed algorithms, static RF interfaces setting is applied to enable the simultaneous use of different interfaces. It is shown that ORSA results are optimal and MRSA results are stable. The simulations compare the capacity of data transmission of proposed and baseline algorithms. The results show that when the number of channels is not restricted, MRSA is only about 3% lower than ORSA and its results are higher than direct transmission and random relay selection, about 56% and 117%, respectively.

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“…These cluster heads transmit data of all other users in its cluster. The proposed algorithm improves the system capacity and energy consumption compared to other similar work [27].…”

Section: A Related Workmentioning

confidence: 84%

“…Recently, a relay selection algorithm based on the Basic Sequential Algorithmic Scheme (BSAS) is proposed for high density LTE networks [27]. Two layers of users are considered, in this work.…”

Section: A Related Workmentioning

confidence: 99%

Novel Relay Selection Algorithms for Machine-to-Machine Communications With Static RF Interfaces Setting

et al. 2020

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“…A relay selection-based clustering scheme for high-density LTE Networks which is based on the Basic Sequential Algorithmic Scheme (BSAS) along with power control protocol is presented in [59]. The proposed scheme does not require any extra infrastructure such as other capacity improving schemes required small cells and relay stations.…”

Section: Comparison Of the Recent Approaches Regarding Manetsmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

2020

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Mobile ad hoc networks (MANETs) have recently received a lot of attention from researchers due to their ease of deployment and versatility. Clustering is one of the most efficient ways to coordinate routing in MANETs to improve network performance using different attributes and metrics. In this paper, the authors have extensively studied clustering schemes and divided the schemes into multiple types based on the Cluster Head (CH) selection criteria, which provides a good understanding of how each type of clustering algorithm differs from each other. The authors analyzed the performance of existing schemes based on the quality of service (QoS) metrics. Based on findings, the authors clarified some important tradeoffs between QoS metrics and also established some important factors influencing the efficiency of clustering schemes. These studies also contain open research challenges and proposed solutions to improve the performance of clustering schemes. INDEX TERMS Clustering, cluster head, MANETs, protocol and schemes.

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“…In Algorithm 2 (1), the thresholds thrho and thbeta of ρ and β are determined by characterizing the decision graph. From (3) to (8), find all nodes with local density greater than thrho and comprehensive distance greater than thbeta.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…In recent years, the CFSDP based on density clustering algorithm proposed by Alex et al is popular with the main idea of adhering to the following two principles in the selection of clustering center: firstly, the density of the clustering center itself is greater than the density of adjacent points; secondly, the distance between the clustering center and other clustering center is relatively long. Based on CFSDP algorithm in combination with scale free of complex networks, the clustering algorithm for fast detection of central nodes proposed in this paper has the advantages of high efficiency of density-based numerical clustering algorithm and no need to set clustering quantitative parameters in advance [8].…”

Section: Introductionmentioning

confidence: 99%

Network Clustering Algorithm Based on Fast Detection of Central Node

Jia

2022

Scientific Programming

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Based on scale-free and density-based complex networks and numerical clustering algorithm, a graph clustering algorithm based on fast detection of central nodes is proposed. Through the calculation of local density and comprehensive clustering of nodes in the network, the clustering center in the network can be found quickly and noncentral nodes can be divided into the clustering center according to the nearest neighbor principle, thus avoiding parameter limitations such as the number of clustering to be set in advance using conventional classic social network detection algorithm. The experimental comparison and analysis in the real network indicate that the graph clustering algorithm based on fast detection of the central node is highly effective and efficient.

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Relay selection based clustering techniques for high density LTE networks

Cited by 3 publications

References 19 publications

Novel Relay Selection Algorithms for Machine-to-Machine Communications With Static RF Interfaces Setting

Novel Relay Selection Algorithms for Machine-to-Machine Communications With Static RF Interfaces Setting

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

Network Clustering Algorithm Based on Fast Detection of Central Node

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