Cluster-based traffic information generalization in Vehicular Ad-hoc Networks

Arkian, Hamid Reza; Atani, Reza Ebrahimi; Pourkhalili, Atefe; Kamali, Saman

doi:10.1016/j.vehcom.2014.08.003

Cited by 41 publications

(30 citation statements)

References 16 publications

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“…This work combined vehicular multihop algorithm for stable clustering‐ long term evolution (VMaSc‐LTE) and IEEE 802.11p for multihop clustering which needs further investigation for urban scenario. Similar hypothesis was also presented in research contributions proposed a solution to take care of connectivity issues in VANET.…”

Section: Related Worksupporting

confidence: 68%

Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks

Kumar

2019

Int J Communication

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Summary Vehicular ad‐hoc networks (VANETs) play a vital role in today's context of vehicular traffic. In this paper, clusters of vehicles are created on the basis of average speed of the vehicles. One cluster communicates with the next cluster through a cluster head and also share the same information with next cluster heads and installed road side units (RSUs). By using this technique, we can solve the problem of rough driving behavior and road terrorism which is due to speed variation of vehicles and fake information dissemination by the drivers. Many a times, drivers may spread fake accident‐related information into the network which is a serious cause of concern in VANETs. It is ensured that such drivers are not allowed to spread wrong information in the network to avoid accidents. To solve this problem, we developed performance enhancement of traffic information gathering (PEnTInG) algorithm that selects only those drivers/vehicles as cluster heads in a cluster who has maximum value of the cluster head factor (CHF). The CHF is derived by considering different weights in range of 0 to 1 of relative average speed, time to leave, trust factor, and neighborhood degree. Further, the elected cluster head shares and stores the same information with the RSUs. In case, a driver wants to disseminate fake or wrong information in a network, then that vehicle driver can be easily tracked by the local authority by accessing RSU data. Simulation results show that the stability of PEnTInG is increased by 25% against the existing schemes viz. lowest‐ID, MCMF, and cluster‐based technique.

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Section: Related Worksupporting

confidence: 68%

Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks

Kumar

2019

Int J Communication

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“…We compare our proposed cluster formation and cluster head selection technique by using K-Means and FloydWarshall algorithm (KMFW) algorithm with Cluster-based traffic information generalization (CTIG) [22] and clustering algorithm in vehicular ad hoc networks (VWCA) [23]. Fig.…”

Section: -40 M/secmentioning

confidence: 99%

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

Hussain¹,

Chen²

2017

ijacsa

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Abstract-Vehicular Ad-hoc Network (VANETs) is the specific form of Mobile ad-hoc networking (MANETs) in which high dynamic nodes are utilized in carrying out the operations. They are mainly used in urban areas for safety traveling. Clustering algorithms are used for clustering the vehicles that are in the range of the network as VANET consists of a great amount of traffic. A clustering head node is used specified through a procedure to collect all information from the surroundings. This study introduced a new method for cluster head selection by using the K-Mean and Floyd-Warshall algorithms. The proposed technique first divided the points for vehicle groups while the Floyd-Warshall algorithm calculated all pairs of shortest distance for every vehicle within the defined cluster. A vehicle with the smallest average distance among a cluster is chosen as the cluster head. The Floyd-Warshall algorithm overall selects a centralized vehicle as a cluster head, hence its stability time will improve significantly.

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“…[12] and [13] provide complex clustering algorithms but they mainly focus on stabilization of clusters itself in order to generalize traffic information and aim at trust modelling respectively. This approach is not suitable in our context of safety related message evaluation which demands for real-time constraints and lightweight computation complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Two basic approaches exist to build up such virtual networks: AI , every vehicle within radio range is used for considering it in evaluation process, and AII , a fully cluster-based approach where vehicles with similar driving states (for instance position, speed, direction) are clustered. Thereby only messages from cluster masters are propagated to other clusters [12,13], whereas data from cluster slaves are not. AI provides highest accuracy in terms of the virtual environment, since every vehicle in range is considered.…”

Section: V2x Message Evaluation Designmentioning

confidence: 99%

A V2X Message Evaluation Methodology and Cross-Domain Modelling of Safety Applications in V2X-enabled E/E-Architectures

Bucher¹,

Buciuman²,

Klimm³

et al. 2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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The introduction of Vehicular Ad-Hoc Networks (VANETs) enables great potential for improving road traffic flow and especially active safety applications such as cooperative adaptive cruise control (CACC). Such applications not only rely on continuous broadcast of vehicle state information (beacons) of all vehicles, but also have strict real-time requirements.Regarding automotive E/E architectures this continuous broadcasting adds heavy internal E/E data traffic that needs to be processed in real-time by Electronic Control Units (ECUs). In this work we address this issue by proposing a novel cluster-based message evaluation methodology to significantly reduce internal E/E network traffic by discarding irrelevant messages. The approach is only depending on information received over beacons. It combines a vehicle clustering strategy as well as network and vehicle state monitoring capabilities in order to correctly evaluate messages under real-time constraints. The proposed methodology is modeled inside an abstract ECU. It is evaluated by simulating a model-based CACC application under different traffic scenarios. It is shown that a significant reduction of messages is achievable, while still guaranteeing accident-free behavior of CACC.

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Cluster-based traffic information generalization in Vehicular Ad-hoc Networks

Cited by 41 publications

References 16 publications

Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks

Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

A V2X Message Evaluation Methodology and Cross-Domain Modelling of Safety Applications in V2X-enabled E/E-Architectures

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