User-Oriented Fuzzy Logic-Based Clustering Scheme for Vehicular Ad-Hoc Networks

Tal, Irina; Muntean, Gabriel‐Miro

doi:10.1109/vtcspring.2013.6692801

Cited by 29 publications

(21 citation statements)

References 20 publications

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“…The neighborhood relationship is built using the position information embedded in the periodic messages broadcasted by vehicles 5 . Vehicles broadcast their current state to all other nodes within their transmission range r. Therefore, two vehicles i and j are considered r-neighbors if the distance between them is less than r. This defines the degree of the node.…”

Section: Network Connectivitymentioning

confidence: 99%

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Cluster Connectivity Assurance Metrics in Vehicular ad hoc Networks

Aissa

Belghith

Bouhdid

2015

Procedia Computer Science

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Many applications introduced by Vehicular Ad-Hoc Networks (VANETs), such as intelligent transportation and roadside advertisement, make VANETs become an important component of metropolitan area networks. In VANETs, mobile nodes are vehicles which are equipped with wireless antennas; and they can communicate with each other by wireless communication on ad-hoc mode or infrastructure mode. Clustering vehicles into different groups can introduce many advantages for VANETs as it can facilitate resource reuse and increase system capacity. The main contribution of our work is a new strategy for clustering a VANET and improvements in many classical clustering metrics. One of the main ideas is the definition of a new optimized selection metric for the clustering of vehicular nodes, in the framework of Next Generation Vehicular ad-hoc Network. These metrics should select clusterheads which provide safe clusters and avoid collisions with adjacent vehicle nodes and intend to create stable clusters by reducing reclustering overhead and prolonging cluster lifetime

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Section: Network Connectivitymentioning

confidence: 99%

“…A novel user-oriented Fuzzy Logic-based k-hop distributed clustering scheme for VANETs that takes into consideration the vehicle passenger preferences is proposed in 5 . The novelty element introduced is the employment of Fuzzy Logic as a prominent player in the clustering scheme.…”

Section: Introductionmentioning

confidence: 99%

Cluster Connectivity Assurance Metrics in Vehicular ad hoc Networks

Aissa

Belghith

Bouhdid

2015

Procedia Computer Science

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“…The maintenance phase is adaptable to drivers' behavior on the road and has a learning mechanism for predicting the future speed and position of all cluster members using fuzzy logic inference system. The second algorithm [17] proposes a novel user-oriented fuzzy logic-based k-hop distributed clustering scheme for VANETs that takes into consideration the vehicle passenger preferences.…”

Section: Related Workmentioning

confidence: 99%

Vehicle interconnection metric and clustering protocol for improved connectivity in vehicular ad hoc networks

Vodopivec

Hajdinjak

Bešter

et al. 2014

J Wireless Com Network

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Communication is the main driving force behind the emerging intelligent transportation systems, which are expected to make traveling safer, more ecological, and faster. The most challenging among all the different communication technologies that will be used are the direct vehicle-to-vehicle communications, because vehicles move with high speeds and in different directions. Apart from that, it is expected that vehicles should be able to organize themselves in an ad hoc network without any assistance from outside entities, such as road side units. To make the ad hoc network less dynamic and communications more reliable, vehicles with similar movement patterns can be grouped together in clusters. Clustering is a well-known method for organizing ad hoc networks and is used in mobile and wireless sensor networks, but with different constraints and goals, so new clustering solutions for vehicular ad hoc networks (VANET) have to be developed. In recent years, this has been a hot topic among researchers and many different clustering algorithms for VANET have been proposed. In this paper, we propose a new clustering metric for VANET, named vehicle interconnection metric, which is based on sending periodic beacons among vehicles and reflects the communication abilities between them. We also propose a new clustering algorithm whose primary goal is increased connectivity and lower number of disconnects. The working principle of this algorithm is also inverted compared to others and uses unneeded cluster head elimination instead of cluster head election. Mathematical analysis of memory usage and communication overhead are provided, predicting low-resource usage. Simulation results, obtained with the ns-3 network simulator and the SUMO vehicle movement simulator, have confirmed the analysis and expected performance in terms of cluster head duration, number of connectivity losses and role switches.

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“…Unlike K-means clustering, the hard clustering method, Fuzzy C-Means (FCM) algorithm [12] is a kind of soft clustering algorithm. In FCM, data elements can belong to more than one cluster, and associated with each element is a set of membership levels, just like the user-oriented fuzzy logic-based clustering algorithm used in research of I. Tal et al [13]. However, centroid-based clustering only finds a local optimum, and the computational overhead if commonly heavy.…”

Section: A Clustering Algorithms In Hotspot Discoverymentioning

confidence: 99%

Hotspot discovery algorithms in coverage selection model over VANETs

Cheng

Xin

Boukerche

et al. 2014

2014 IEEE Global Communications Conference

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The RSU deployment algorithms to cover interest regions have emerged as promising areas of research in vehicular ad hoc networks. However, there is few research on the discovery of the interest regions. Besides, the deployment algorithms are usually proposed for either continuous coverage or sparse coverage. The wisdom of the model selection are rarely addressed. In this paper, we proposed a new algorithm to discover interest regions. A budget-constrained coverage selection algorithm is also presented to help network designers choose suitable coverage models to meet the budget and quality requirements. The algorithms are implemented on top of Ns2 and the simulations are carried out using SUMO and OpenStreet Maps. The performance comparison between our algorithm and other two clustering algorithms prove that our algorithm has a better performance in terms of contact time for both sparse coverage and continue coverage.

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User-Oriented Fuzzy Logic-Based Clustering Scheme for Vehicular Ad-Hoc Networks

Cited by 29 publications

References 20 publications

Cluster Connectivity Assurance Metrics in Vehicular ad hoc Networks

Cluster Connectivity Assurance Metrics in Vehicular ad hoc Networks

Vehicle interconnection metric and clustering protocol for improved connectivity in vehicular ad hoc networks

Hotspot discovery algorithms in coverage selection model over VANETs

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