Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow

Chen, Yuzhong; Fang, Mingyue; Shi, Siyu; Guo, Wenzhong; Zheng, Xianghan

doi:10.1186/s13638-015-0327-0

Cited by 82 publications

(42 citation statements)

References 24 publications

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“…A Distributed Multi-hop Clustering algorithm based on Neighborhood Follow (DMCNF) has been presented to enhance robustness in clustering algorithms [32]. The DMCNF basically focuses on how the vehicular cloud nodes communicate among each other to improve efficiency.…”

Section: Distributed Clusteringmentioning

confidence: 99%

“…Therefore, the issue need thorough exploration. [32][33][34][35][36][37][38][39][40][41][42][43][44] by suggesting solutions to handle various type of data dissemination issues using clustering, data-centric and other routing approaches, yet many issues have not covered, such as, location verification, data dissemination and video centric routing. The conventional VANETs routing [69][70][71] may not be suitable in the case of CC-V due to the connectivity challenges in mobility.…”

Section: Future Research Challengesmentioning

confidence: 99%

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Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Aliyu

Abdullah

Kaiwartya

et al. 2017

IETE Technical Review

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Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.

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Section: Distributed Clusteringmentioning

confidence: 99%

Section: Future Research Challengesmentioning

confidence: 99%

Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Aliyu

Abdullah

Kaiwartya

et al. 2017

IETE Technical Review

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“…The authors in [9] proposed a multi-hop based cluster technique in order to progress stability. This method allows vehicles to choose their routes from one-hop neighbors.…”

Section: Related Workmentioning

confidence: 99%

Message Dissemination in Vehicular Networks on the Basis of Agent Technology

Harrabi

Jaâfar

Ghédira³

2017

Wireless Pers Commun

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Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road. That's why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs, but most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.

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“…The major drawback here is it does not consider the mobility of the vehicles in selecting the cluster heads. In [11], a Distributed Multi-Hop Clustering Scheme for VANETs based on a neighborhood Follow (DMCNF). In this algorithm the vehicles follow their one-hop neighbors according to these factors: the historical cluster membership, relative mobility, and the number of followers.…”

Section: Related Workmentioning

confidence: 99%

Improved Hybrid Model in Vehicular Clouds based on Data Types (IHVCDT)

Khawatreh¹,

Al-Zubi²

2017

ijacsa

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Abstract-In Vehicular Cloud (VC), vehicles collect data from the surrounding environment and exchange this data among the vehicles and the cloud centers. To do that in an efficient way first we need to organize the vehicles into clusters, each one works as a VC, and every cluster is managed by the cluster head (broker). The vehicles are grouped in clusters with adaptive size based on their mobility and capabilities. This model is an approach that forms the clusters based on the vehicles capabilities and handles with different types of data according to its importance to select the best route. A hybrid model is proposed to deal with these differences; Long-Term Evolution (LTE) is used with IEEE 802.11P which forms the traditional wireless access for Vehicular Ad hoc Networks (VANETs). This merge gives the high data delivery, wide-range transmission, and low latency. However, using only LTE based VANET is not practical due to its high cost and the large number of occurrences in the base stations. In this paper, a new Vehicular Cloud (VC) model is proposed which provides data as a service based on Vehicular Cloud Computing (VCC). A new method is proposed for high data dissemination based on the data types. The model is classified into three modes: the urgent mode, the bulk mode, and the normal mode. In the urgent mode, Long-Term Evolution (LTE) is used to achieve a high delivery with minimum delay. In the bulk mode, the vehicle uses IEEE 802.11p and chooses two clusters to divide this huge data. In the normal mode, the model works as D-hops cluster based algorithm.

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Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow

Cited by 82 publications

References 24 publications

Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Message Dissemination in Vehicular Networks on the Basis of Agent Technology

Improved Hybrid Model in Vehicular Clouds based on Data Types (IHVCDT)

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