Cluster-based Data Collection Scheme for Vehicular Ad-hoc Networks

Touil, Abdelali; Sbai, Asma; Ghadi, Fattehallah

doi:10.1016/j.procs.2019.01.009

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…However, the stability performance is evaluated based on overhead only which is not even a significant parameter for stability. The authors of [77] also proposed a clustering-based data collection scheme [78] to control congestion where only the CH can communicate with RSU. Density, velocity, and direction are considered to select the CH.…”

Section: Data Dissemination/ Routing Algorithmsmentioning

confidence: 99%

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

Mukhtaruzzaman,

Atiquzzaman

2023

Computer Networks

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Section: Data Dissemination/ Routing Algorithmsmentioning

confidence: 99%

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

Mukhtaruzzaman,

Atiquzzaman

2023

Computer Networks

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“…In the literature, several contributions propose different architectures and schemes to support efficient data collection. In [33], Touil et al propose a data collection scheme based on a clustering approach; the objective is to reduce the impact of mobility and density on the data collection stations. Khan et al [34] propose a data forwarding algorithm for data collection; it is based on a ranking scheme of On-Board Units (OBUs) and the hop count of data traffic.…”

Section: ) Data Collectionmentioning

confidence: 99%

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Lamssaggad¹,

Benamar²,

Hafid³

et al. 2021

IEEE Access

112

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With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system-Intelligent Transportation Systems (ITS)-that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This paper gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this paper provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

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“…An obvious drawback is the need to tune the value of the strength of the two terms to make a tradeoff between cluster stability (caused by the first term) and delay (caused by the second term). The selection of cluster heads usually use a multi-criteria approach which is based on delay, relative velocity and other factors that have been used by other studies [37]. Another work that has used a meta-heuristic approach for the VANETs clustering is the work of [38] in which honey bee optimization was integrated with the genetic algorithm to construct VANETs clustering in a distribute way.…”

Section: Literature Surveymentioning

confidence: 99%

A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

Talib

Hassan

Alamery³

et al. 2020

IEEE Access

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VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into subgroups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively. INDEX TERMS vehicular ad hoc networks, VANETs clustering, Center-based clustering, evolving clustering, grid based-clustering.

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Cluster-based Data Collection Scheme for Vehicular Ad-hoc Networks

Cited by 6 publications

References 13 publications

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

A Survey on the Current Security Landscape of Intelligent Transportation Systems

A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

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