Impact of Random Mobility on the Inhomogeneity of Spatial Distributions

Gyarmati, M.; Schilcher, Udo; Brandner, Günther; Bettstetter, Christian; Chung, Yun Won; Kim, Young Han

doi:10.1109/glocom.2008.ecp.85

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…Our model not only manages to preserve a desired level of heterogeneity over time [20], but allows for tunning the desired spatial density for evaluating network protocols under different levels of node density, according to the researchers needs. In case the objective is to simulate some specific real scenario, information on trace sources and repositories can be found in [21] and can be used to generate the IM as described here.…”

Section: B Extracting Information From Tracesmentioning

confidence: 99%

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On the Invariance of Spatial Node Density for Realistic Mobility Modeling

Astuto

2011

2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems

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In this paper we show that human mobility exhibits "persistent" behavior in terms of the spatial density distribution of the mobile nodes over time. Using real mobility traces, we observe that the original non-homogeneous node spatial density distribution, where some regions may be quite dense while others may be completely deserted, is maintained at different instants of time. We also show that mobility models that select the next node position based on the position of other nodes, a la "preferential attachment", do not preserve the original spatial node density distribution and lead to behavior similar to random mobility as exemplified by the Random Waypoint model. To the best of our knowledge, this is the first time that these phenomena have been reported. Based on these observations, we propose a simple mobility model that preserves the desired spatial density distribution. Moreover, when simulating the operation of a network moving according to the proposed model, we found that performance results expressed by a number of network metrics also match closely results obtained under mobility governed by real traces. We also compare our results to models whose steady-state do not preserve the original non-homogeneous density distribution and show that network performance under such regimes deviates from performance under real trace mobility.

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Section: B Extracting Information From Tracesmentioning

confidence: 99%

“…In [19], they propose a method that creates initial non-homogeneous node distributions and in [20], a variant of RWP mobility that maintains the non-homogeneity of an original node distribution.…”

Section: Related Workmentioning

confidence: 99%

On the Invariance of Spatial Node Density for Realistic Mobility Modeling

Astuto

2011

2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems

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“…They propose a method that creates initial non-homogeneous node distributions and in [3], authors analyze the impact of random mobility in the inhomogeneity of spatial distributions via simulations.…”

Section: Related Workmentioning

confidence: 99%

“…To date, only a few efforts have focused on modeling spatial density. Notable examples include [3,4,5]. However, most previous work have been focusing exclusively on the Random Waypoint (RWP) model [1].…”

Section: Introductionmentioning

confidence: 97%

Modeling spatial node density in waypoint mobility

Astuto

2012

2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012)

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This paper introduces a modeling framework to analyze spatial node density in mobile networks under "waypoint"-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first node density modeling framework generic enough that can be applied to any "waypoint"-based mobility regime.

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“…Even though the initial distribution is uniform, the spatial distribution may change during the process of simulation. The changing effects of inhomogeneity on three well known mobility models, that is random waypoint (RWP) model [5], random direction (RD) model [6] and reference point group mobility (RPG) model [7], have been analyzed in literature [8]. Simulations show that the inhomogeneity property of initial spatial distribution may change after a few seconds.…”

Section: Introductionmentioning

confidence: 99%

A Modified Inhomogeneity Measure in Spatial Distribution

Zhang

et al. 2013

2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

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In this paper, we investigated the traditional inhomogeneity measure and proposed a modified inhomogeneity measure. The traditional inhomogeneity measure adopted absolute value as norm. However, it was not accurate enough when used in sparse spatial distribution. The modified inhomogeneity measure adopted Euclidean distance instead of absolute value. An example was taken to compare the traditional inhomogeneity measure and the modified inhomogeneity measure, and verified the modified inhomogeneity measure was more accurate in sparse spatial distribution. Simulation compared the inhomogeneity measured by different methods, and the results showed that the modified inhomogeneity measure better fits human perception.

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Impact of Random Mobility on the Inhomogeneity of Spatial Distributions

Cited by 10 publications

References 12 publications

On the Invariance of Spatial Node Density for Realistic Mobility Modeling

On the Invariance of Spatial Node Density for Realistic Mobility Modeling

Modeling spatial node density in waypoint mobility

A Modified Inhomogeneity Measure in Spatial Distribution

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