Spatial Node Distribution of Manhattan Path Based Random Waypoint Mobility Models with Applications

Crescenzi, Pilu; Ianni, Miriam Di; Marino, Andrea; Rossi, Gianluca; Vocca, Paola

doi:10.1007/978-3-642-11476-2_13

Cited by 8 publications

(27 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We observe that, by taking the limit for ? 0, our explicit formula of the spatial distribution coincides to that computed by using rather complex integral calculus in [10] for the continuous space-time MRWP model (in terms of probability density functions). Finally, we give, for the first time, the destination distribution of the continuous space-time MRWP model as well.…”

Section: Introductionmentioning

confidence: 68%

“…This is also the formula obtained in [10] for the classic (real time-space) MRWP model. The stationary destination density function can be computed very similarly by applying Eq.…”

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 87%

“…This infinite process yields the Manhattan Random Way-Point. Even though this mobility model may look simple, the analytical study of its stationary properties has required very complex integral calculus and only its stationary spatial distribution has been derived till now [10].…”

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 99%

“…In this section, we study a mobility model, called Manhattan Random Way-Point, an interesting and naural version of the Random Way-Point that has been recently studied in [10].…”

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 99%

“…In the first one, we consider the Manhattan Random Way-Point (MRWP) model [10,3,15]. This version of the Random Way-Point model is motivated by scenarios where agents travel over an urban zone and try to minimize the number of turns while keeping the chosen route as short as possible.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modelling mobility: A discrete revolution

Clementi¹,

Monti

Silvestri

2011

Ad Hoc Networks

View full text Add to dashboard Buy / Rent full text

We introduce a new approach to model and analyze mobility. It is fully based on discrete mathematics and yields a class of mobility models, called the Markov Trace model. It can be viewed as the discrete version of the Random Trip model: including all variants of the Random Way-Point model [15]. We derive fundamental properties and explicit analytical formulas for the stationary probability distributions yielded by the Markov Trace model. Besides having a per-se interest, such results can be exploited to compute formulas and properties for concrete cases by just applying counting arguments. We apply the above general results to the discrete version of the Manhattan Random Way-Point. We get explicit formulas for the total stationary distribution and for two important conditional distributions: the agent spatial and the agent destination ones. Our method makes the analysis of complex mobile systems a feasible task. As a further evidence of this important fact, we model a complex vehicular-mobile system over a set of crossing streets. Several concrete issues are implemented such as parking zones, traffic lights, and variable vehicle speeds. By using a modular version of the Markov Trace model, we get explicit formulas for the stationary distributions yielded by this vehicular-mobile model as well. (C) 2010 Elsevier B.V. All rights reserved

show abstract

Section: Introductionmentioning

confidence: 68%

“…This is also the formula obtained in [10] for the classic (real time-space) MRWP model. The stationary destination density function can be computed very similarly by applying Eq.…”

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 87%

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 99%

“…In this section, we study a mobility model, called Manhattan Random Way-Point, an interesting and naural version of the Random Way-Point that has been recently studied in [10].…”

Section: Application I: the Manhattan Random Way-point Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations