Weight Clustering Based TDMA-MAC Scheme in VANET

Xie, Jianli; Li, Cuiran

doi:10.7305/automatika.2016.07.885

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Several such algorithms have been proposed for VANETs and are described below. A new weighted clustering TDMA-MAC scheme (WCS) for VANETs is proposed in Xie et al, 56 When selecting the set of CHs, the WCS algorithm uses indexes and an entropy weight that combines certain parameters, such as radio signal transmitting power constraints, vehicle energy consumption, and vehicle mobility. Based on the TDMA-MAC technique, a realistic clustering channel access mechanism is introduced for reducing the chance of collision and ensuring efficient end-to-end communication in VANETs.…”

Section: Weight-based Clustering Algorithmsmentioning

confidence: 99%

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Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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Summary Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area.

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Section: Weight-based Clustering Algorithmsmentioning

confidence: 99%

“…Density Stability Latency Overhead Convergence WCS 56 Medium Medium Medium High Low ACA 57 Low Medium Low Medium Low AWCP 58, 58 High High Medium Medium Medium MFCA-IoV 59 Medium High Low High Low CAVDO 29 High High Low Medium Medium WECA-MR 22 Low Medium Medium Medium Low EWCA 60 High High Low High Medium…”

Section: Algorithmmentioning

confidence: 99%

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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“…Many intelligent clustering schemes for VANETs were elaborated. 12,[24][25][26] Unfortunately, all these works apply passive intervehicle distances as one of main metrics used to select CHs. That is, these distances do not take into consideration the safe intervehicle distances which assist drivers to avoid collisions thus generating clusters' instability and frequent CHs reelections leading to higher overheads.…”

Section: Contributionmentioning

confidence: 99%

SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

Aissa

Bouhdid

Mnaouer

et al. 2020

Trans Emerging Tel Tech

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Vehicular ad hoc network (VANET) nodes are characterized by their high mobility and by exhibiting different mobility patterns. Therefore, VANET clustering schemes are required to account for the mobility parameters among neighboring nodes to produce relatively stable clustering schemes. In this article, we propose a novel cluster-head (CH) selection scheme for VANETs. This scheme is based on a fuzzy logic-powered, k-hop distributed clustering algorithm. It deals efficiently with scalability and stability issues of VANETs and is able to achieve highly stable clustering topologies as compared with other schemes. Our proposed clustering scheme strives to maintain a safe intervehicle distance as a one prime metric for CH selection. Moreover, a major contribution of our work is the proposal of a novel strategy for constructing fuzzy logic-based clustering algorithms useful for VANETs. This proposed solution is useful in an Internet of things-based setting that involves controlled vehicle-to-vehicle communication. We first derive mathematically, a new average distance estimation formula that is used as a metric for selecting CHs, leading to safer clusters that avoid collisions with front and rear vehicles. Furthermore, the new proposed scheme creates stable clusters by reducing reclustering overhead and prolonging clusters' lifetimes.

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“…The total number of vehicles directly connected to vehicle i is called its connectivity level. In general, the number of neighbours of node i at time t in a clusteras given by [37] is calculated as follows: …”

Section: System Modelmentioning

confidence: 99%

“…the vehicles travelled towards the same direction with a mean velocity of 30 m/s and mean deviation of 5 m/s in a free traffic flow. Additionally, the weight factor value associated with each metric for the CH election process was arbitrarily defined on the basis of the importance of each metric[37, 40]. The weight value for speed was 0.4 while that for the remaining metrics including the position and the number of neighbouring nodes was 0.3 each.…”

Section: Simulationmentioning

confidence: 99%

Enhanced weight-based clustering algorithm to provide reliable delivery for VANET safety applications

et al. 2019

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A vehicular ad hoc network (VANET) is an emerging and promising wireless technology aimed to improve traffic safety and provide comfort to road users. However, the high mobility of vehicles and frequent topology changes pose a considerable challenge to the reliable delivery of safety applications. Clustering is one of the control techniques used in VANET to make the frequent topology changes less dynamic. Nevertheless, research has shown that most of the existing clustering algorithms focus on cluster head (CH) election with very few addressing other critical issues such as cluster formation and maintenance. This has led to unstable clusters which could affect the timely delivery of safety applications. In this study, enhanced weight-based clustering algorithm (EWCA) was developed to address these challenges. We considered any vehicle moving on the same road segment with the same road ID and within the transmission range of its neighbour to be suitable for the cluster formation process. This was attributed to the fact that all safety messages are expected to be shared among the vehicles within the vicinity irrespective of their relative speedto avoid any hazardous situation. To elect a CH, we identified some metrics on the basis of the vehicle mobility information. Each vehicle was associated with a predefined weight value based on its relevance. A vehicle with the highest weight value was elected as the primary cluster head (PCH). We also introduced a secondary cluster head (SeCH) as a backup to the PCH to improve the cluster stability. SeCH took over the leadership whenever the PCH was not suitable for continuing with the leadership. The simulation results of the proposed approach showed a better performance with an increase of approximately40%– 45% in the cluster stability when compared with the existing approaches. Similarly, cluster formation messages were significantly minimized, hence reducing the communication overhead to the system and improving the reliable delivery of the safety applications.

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Weight Clustering Based TDMA-MAC Scheme in VANET

Cited by 10 publications

References 12 publications

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

Enhanced weight-based clustering algorithm to provide reliable delivery for VANET safety applications

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