Connectivity in cooperative wireless ad hoc networks

Wang, Liaoruo; Liu, Benyuan; Goeckel, Dennis; Towsley, Don; Westphal, Cédric

doi:10.1145/1374618.1374636

Cited by 27 publications

(18 citation statements)

References 9 publications

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“…They are respectively: 1) the first and second signals are totally in phase; 2) the first signal is 15 s lag behind the second one; and 3) the first signal is 30 s lag behind the second one. To evaluate the stochastic independence of the model with vehicle interactions, we examine the dependency of the number of cars in two nonoverlapping regions with vehicle interactions, raise the arrival rate of vehicles to the urban route in steps from 10 to 30 cars/min, and examine the correlation between the number of vehicles in two consecutive regions (2, 2.2] and (2.2, 2.4] in time interval (3,5]. We can see from Fig.…”

Section: B Evaluation With Cascaded Traffic Signalsmentioning

confidence: 99%

“…To illustrate how the stochastic traffic model can be used to identify a poorly connected region and facilitate the infrastructure node placement process, we explore connectivity in the road segment (2, 2.5] time-averaged over the time interval (3,5] in the two-traffic-light scenario described in Section V-B. We consider vehicles arrive at constant rates (denoted by ) of 30 and 3 cars/min to respectively represent the peak-hour and off-peak-hour traffic.…”

Section: Applications Of the Stochastic Traffic Modelmentioning

confidence: 99%

“…[2] investigates the radio range assignment problem and obtains bounds for the probability that a node is isolated and the network is connected on a one-dimensional (1-D) line. On the other hand, [3] examines the node density threshold for achieving full connectivity in both 1-D and two-dimensional (2-D) ad hoc network. [4] and [5] study the relation between the minimum node degree and -connectivity in a random graph and explore the minimum radio transmission range for achieving a fully connected ad hoc network for a given node density.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Model and Connectivity Dynamics for VANETs in Signalized Road Systems

Leung

Polak

2011

IEEE/ACM Trans. Networking

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Abstract-The space and time dynamics of moving vehicles regulated by traffic signals governs the node connectivity and communication capability of vehicular ad hoc networks (VANETs) in urban environments. However, none of the previous studies on node connectivity has considered such dynamics with the presence of traffic lights and vehicle interactions. In fact, most of them assume that vehicles are distributed homogeneously throughout the geographic area, which is unrealistic. We introduce in this paper a stochastic traffic model for VANETs in signalized urban road systems. The proposed model is a composite of the fluid model and stochastic model. The former characterizes the general flow and evolution of the traffic stream so that the average density of vehicles is readily computable, while the latter takes into account the random behavior of individual vehicles. As the key contribution of this paper, we attempt to approximate vehicle interactions and capture platoon formations and dissipations at traffic signals through a density-dependent velocity profile. The stochastic traffic model with approximation of vehicle interactions is evaluated with extensive simulations, and the distributional result of the model is validated against real-world empirical data in London. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the knowledge of the mean vehicular density dynamics and its probability distribution from the stochastic traffic model, we determine the degree of connectivity in the communication network and illustrate that system engineering and planning for optimizing both the transport (in terms of congestion) and communication networks (in terms of connectivity) can be carried out with the proposed model. Index Terms-Connectivity, signalized road system, stochastic traffic model, vehicle interaction, vehicular ad hoc network (VANET).

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Section: B Evaluation With Cascaded Traffic Signalsmentioning

confidence: 99%

Section: Applications Of the Stochastic Traffic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Model and Connectivity Dynamics for VANETs in Signalized Road Systems

Leung

Polak

2011

IEEE/ACM Trans. Networking

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“…In [18], authors investigate the connectivity problem when directional antennas are used. While authors of [19] consider how physical layer cooperation can be used to improve the connectivity in wireless ad hoc networks.…”

Section: Related Workmentioning

confidence: 99%

Impact of Nakagamim Fading Model on Multihop Mobile Ad Hoc Network

Tarique¹,

Tanvir²

2011

IJCA

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Several theoretical and experimental based models have been proposed to predict the fading envelope of the received signal in multipath condition. In this paper, we considered a model named Nakagami-m model. Nakagami-m can model a variety of fading environments, where it closely approximates the Nakagami-q (Hoyt) and the Nakagami-n (Rice) models, and has the Rayleigh and one sided Gaussian models as special cases. This model provides the best fit to land-mobile system like wireless mobile ad hoc Network (MANET). Under Nakagami-m fading model, received packet may not be clearly understood by the receiving node, which affects the routing protocol as well as the medium access control protocol of a network. The severity of Nakagami-m fading model on the network performance has been presented in this paper which is demonstrated via simulation results. Simulation results illustrate that the performance of a network may become unable to meet the expectation if Nakagami-m fading model is used in contrast to the simple two-ray model. A physical layer solution and a Medium Access Control (MAC) layer solution been proposed in this paper to overcome the effects of Nakagami-m fading model. Simulation results prove that these two solutions condense the Nakagami-m fading effect and improve network performance.

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“…Putting these together, it is of great interest to investigate the connectivity in cooperative networks with selfish nodes. Unfortunately, for cooperative networks, the connectivity problem and selfish behavior are separately considered [1], [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Connectivity in Selfish, Cooperative Networks

2010

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Abstract-This paper studies the connectivity of large cooperative ad hoc networks. Unlike existing work where all nodes are assumed to transmit cooperatively, the cooperative network we consider is realistic as we assume that not all nodes are willing to collaborate when relaying other nodes' traffic. For such selfish, cooperative network, we use stochastic geometry and percolation theory to analyze the connectivity and provide an upper bound of critical node density when the network percolates.

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Connectivity in cooperative wireless ad hoc networks

Cited by 27 publications

References 9 publications

Stochastic Model and Connectivity Dynamics for VANETs in Signalized Road Systems

Stochastic Model and Connectivity Dynamics for VANETs in Signalized Road Systems

Impact of Nakagamim Fading Model on Multihop Mobile Ad Hoc Network

Connectivity in Selfish, Cooperative Networks

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