Calculating mobility parameters for a predefined stationary user distribution

Kraaier, J.; Killat, Ulrich

doi:10.1109/icon.2004.1409083

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…Details of the methodology are given in Reference [25]. It uses linear programming, like in our previous work [26], and expresses the user distribution as equality constraints while minimising the flow of users turning around at intersections. It is similar to Reference [23] where parameters of the basic RD model are changed to fit statistics of the RWP model or measured traces.…”

Section: Equalising the User Distributionmentioning

confidence: 99%

Random direction or random waypoint? A comparison of mobility models for urban environments

Kraaier¹,

Killat²

2007

Trans Emerging Tel Tech

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SUMMARYThe Random Direction (RD) and the Random Waypoint (RWP) mobility models have been discussed extensively in the literature. The focus of this discussion was the stationary distribution of users and speeds of users. In this paper, we extend the investigation to street-based variants of the two mobility models. We compare statistical properties including user distribution, sojourn times in hotspots and link durations in ad hoc networks. In order to motivate the use of street-based mobility models, we also perform a comparison with the basic models. Finally, we present our approach to achieve identical user distributions in both streetbased models and investigate the remaining differences, which prove to be negligible for wireless network simulations.

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Section: Equalising the User Distributionmentioning

confidence: 99%

Random direction or random waypoint? A comparison of mobility models for urban environments

Kraaier¹,

Killat²

2007

Trans Emerging Tel Tech

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“…In an earlier work [8], we showed how the pixel oriented mobility model by Peirera et al [9] can be used to achieve stationary user distributions that match the prescribed distributions exactly. The resulting memory-less movement of the user on a pixel by pixel basis, however, did not allow an appropriate modeling of vehicular movement where a change of direction is only possible at fixed locations.…”

Section: Introductionmentioning

confidence: 97%

mentioning

confidence: 99%

“…The prescribed stationary user distribution is the result of a long simulation run using the Random Waypoint City Model[8]. The displayed values are the sum of the two opposing flows which is the stationary number of users multiplied by the mean speed on the street and divided by its length as given in(10).from[8], are w 3539 = 0.3, w 4548 = 0.7, w 5403 = 0.3, w 12601 = 0.7. According to (20), P c = 10 can be calculated as10 = 0.5 · (0.63 s −1 · 0.3T 3539 + 0.14 s −1 · 0.7T 4548 + 0.59 s −1 · 0.3T 5403 + 0.28 s −1 · 0.7T 12601 ).Assuming T 3539 = T 5403 and T 4548 = T 12601 , we can either choose T 3539 = T 5403 ∈ [0; 54.64] and calculate T 4548 and T 12601 from that choice or choose T 4548 = T 12601 ∈ [0; 68.03] and calculate T 3539 and T 5403 from that choice.…”

mentioning

confidence: 99%

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Controlling the Stationary Distribution of Mobile Users in Wireless Network Simulations

Kraaier

Killat

2007

2007 IEEE 66th Vehicular Technology Conference

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In order to be able to simulate wireless networks in a non-uniform environment resulting in specific user distributions, it is essential to predict and control the stationary user distribution of the employed mobility model. We propose a framework to calculate turning probabilities at intersections of a graph-based mobility model to realize an arbitrary stationary user distribution on a street network. In this way, the user distribution can be fitted to a measured distribution. Our framework is able to cope with different average velocities on streets as well as different pausing probabilities at intersections. We evaluate its scope with the help of an example distribution on a European city center map featuring different street types and a variety of different crossings.

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“…This new framework for mobility simulation aims at providing a restricted movement which is possible with the pixel oriented mobility model [5] as shown in [3] while maintaining a destination-directed movement. Our approach combines aspects of the Random Waypoint Mobility Model with the vector street maps used in [4] and is therefore named Random Waypoint City Model (RaWaCi).…”

Section: Introductionmentioning

confidence: 99%