A New Distributed Mobility-Based Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks in Highway Scenarios

Huang, Ke; Hu, Bin‐Jie

doi:10.1109/vtcfall.2019.8891285

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…In Table 1, several VANET clustering algorithms and the number of citations for each algorithm are highlighted, which have been presented from 2010 to 2022. We can note that the Passive Multi-hop Clustering (PMC) in [14] has the highest mean citations.…”

Section: Vanets Clustering Algorithms Historymentioning

confidence: 99%

“…PMC algorithm in VANET was proposed in [14] to solve the lack in clustering algorithms performance in terms of stability and reliability. In this algorithm, the clustering is introduced depending on the priority neighbor following strategy, and the CH selecting technique is adopted to select the optimal CH.…”

Section: Multi-hop Clustering Algorithmmentioning

confidence: 99%

“…The CH with the higher average relative speed relinquishes his CH job and becomes a CM for the CH with the lower average relative speed. Also, the PMC algorithm in [14] used the cluster merging method in the cluster maintenance phase, the CH node sends merge request packets to other neighboring CH to request cluster merging. If one of these two CHs has smaller following vehicles and high relative speed, the merging process is performed.…”

Section: Multi-hop Clustering Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Jabbar¹,

Trabelsi

2022

Int. J. Interact. Mob. Technol.

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The Vehicular Ad-hoc Network (VANET) represents a new future for dynamic information dissemination between societies. VANET has a wide range of applications in a variety of aspects, including Intelligent Transportation Systems (ITS). VANET has some characteristics, like highly dynamic topology and intermittent connections. These characteristics lead to untrustworthy information transmission in VANET. Vehicle clustering is an efficient approach to improve the scalability of the network and connection reliability. The performance of the clustering is also affected by VANET characteristics. This article provides a comprehensive description of VANET clustering algorithms. The most notable clustering algorithms introduced between 1999 and 2021 are reviewed. A complete survey on clustering in VANETs is provided based upon the clustering process. The clustering process in most algorithms is explored in the aspects of CH selection metrics, cluster formation, and cluster maintenance. The clustering techniques based on some parameters like stability, convergence, overhead, and latency are compared. Some of the most common problems, as well as the approaches employed to solve them, are also discussed. Also, the performance parameters which evaluate the clustering approaches are summarized.

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Section: Vanets Clustering Algorithms Historymentioning

confidence: 99%

Section: Multi-hop Clustering Algorithmmentioning

confidence: 99%

Section: Multi-hop Clustering Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Jabbar¹,

Trabelsi

2022

Int. J. Interact. Mob. Technol.

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“…Recently, more attention was payed to consider more complicated and realistic environments such as urban [19, 20] and deserts [21]. Regarding the coverage of the cluster, multi‐hope clustering, as in [22, 23], is introduced to reduce the number of clusters (NoC) and generate wider clusters at the expense of the added complexity and cluster maintenance overhead, compared to traditional one‐hope clustering. Lately, due to the demonstrated enhancement resulted from the usage of AI in VANET protocols, a kind of intelligence was emerged to clustering in VANETs.…”

Section: Related Workmentioning

confidence: 99%

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Alsuhli

Fahmy²,

Khattab³

2020

IET intell. transp. syst

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“…On the other hand, ML-based clustering algorithms can provide effective CH selection and improve Quality-of-Service (QoS) routing [6]. Adaptive learning techniques can interact with the vehicular environment and address the ample state space of VANETs [7]. Thus, this research aims to address the routing issues by leveraging clustering-based VANETs and developing a novel self-adaptive learning technique for an efficient routing protocol in VANETs.…”

Section: Introductionmentioning

confidence: 99%

AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network

Nahar

Sikarwar

Das

2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges. Experimental results show that AlcFier performs efficiently, improves cluster stability, reduces transmission delays, and improves throughput compared with the state-of-the-art routing protocols.

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A New Distributed Mobility-Based Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks in Highway Scenarios

Cited by 8 publications

References 14 publications

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network

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