Robust clustering for connected vehicles using local network criticality

Li, Weiwei; Tizghadam, Ali; Leon-Garcia, Alberto

doi:10.1109/icc.2012.6364718

Cited by 23 publications

(15 citation statements)

References 8 publications

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“…In [4] we have empirically used τi = 1 n j τij as the local criticality of a node, and we used it to determine the cluster head. Since τij is a Euclidian distance metric (see [1]), τi is an indication of the average distance of node i to its neighbors.…”

Section: Discussionmentioning

confidence: 99%

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Network criticality in vehicular networks

Tizghadam

Leon-Garcia

2012

SIGMETRICS Perform. Eval. Rev.

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Network criticality (resistance distance) is a graph-theoretic metric that quantifies network robustness, and that was originally designed to capture the effect of environmental changes in core communication networks. This paper establishes a relationship between information centrality and network criticality and provides a justification for using the average network criticality of a node to quantify the nodes relative importance in a graph.This results provides a basis for designing robust clustering algorithms for vehicular networks.

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Section: Discussionmentioning

confidence: 99%

“…In a previous experimental work [4], we have introduced the average point-to-point criticality of the links incident to a node i (i.e. τi = 1 n j τij) as a representative of the node importance (referred to as local node criticality).…”

Section: Vanet and Network Criticalitymentioning

confidence: 99%

Network criticality in vehicular networks

Tizghadam

Leon-Garcia

2012

SIGMETRICS Perform. Eval. Rev.

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“…This progressively results in better cluster heads being selected, while resisting short-lived changes in cluster head selection metrics. In Criticality-based Clustering Algorithm (CCA) [31] and G-DMAC [27], a hysteresis threshold is used to prevent thrashing of the cluster structure; a cluster member will only change to a new cluster head if the proposed new cluster head's selection metric exceeds that of the current cluster head by a set threshold. Aggregate Local Mobility (ALM) [33] employs a contention timer to prevent reclustering when two clusters move within communication range of each other, and to avoid the formation of single-member clusters.…”

Section: A Cluster Head Selection Strategymentioning

confidence: 99%

“…CCA [31] defines a metric denoted network criticality as "the normalised random walk betweeness of a node on the network" (sic). That is, how often a node is visited when traversing the network from a source to destination.…”

Section: Cluster Head Selection Criteriamentioning

confidence: 99%

A Comparative Survey of VANET Clustering Techniques

Cooper

Franklin

Ros

et al. 2017

IEEE Commun. Surv. Tutorials

396

176

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A vehicular ad hoc network (VANET) is a mobile ad hoc network (MANET) in which network nodes are vehicles -most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organised nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, inforomation dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcomingthe lack of realistic vehicular channel modelling -is identified. The importance of a rigorous and standardised performance evaluation regime utilising realistic vehicular channel models is demonstrated.

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“…The paper [11] proposes a robust criticality-based clustering algorithm (CCA) based on the concept of the network criticality. Network criticality is a global metric on an undirected graph, which quantifies the robustness of the graph against environmental changes such as topology.…”

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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Robust clustering for connected vehicles using local network criticality

Cited by 23 publications

References 8 publications

Network criticality in vehicular networks

Network criticality in vehicular networks

A Comparative Survey of VANET Clustering Techniques

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

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