Abstract
AbstractThe study and characterization of node mobility in wireless networks is extremely important to foresee the node distribution in the network, enabling the creation of suitable models, and thus a more accurate prediction of performance and dependability levels.In this paper we adopt a structured Markovian formalism, namely SAN (Stochastic Automata Networks), to model and analyze two popular mobility models for wireless networks: the Random Waypoint and Random Direction.Our modeling considers mobility over a discrete space, i.e., over a space divided in a given number of slots, allowing a suitable analytical representation of structured regions. We represent several important aspects of mobility models, such as varying speed and pause times, and several border behaviors that may take place. One, two, and three-dimensional models are presented. For the two-dimensional models, we show that any regular or irregular convex polygon can be modeled, and we describe several routing strategies in two dimensions.In all cases, the spatial node distribution obtained from the steady state analysis is presented and whenever analogous results over continuous spaces were available in the literature, the comparison with the ones obtained in this paper is shown to be coherent.Besides showing the suitability of SAN to model this kind of reality, the paper also contributes to new findings for the modeled mobility models over a noncontinuous space.
Highlights
Mobility keeps gaining in importance, playing a significant role in the design and implementation of communications infrastructures and distributed systems
To the Random Waypoint, the Random Direction model is used in he evaluation of MANET routing protocols such as Ad-hoc On-Demand Distance Vector Routing (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR) and Temporally-Ordered Routing Algorithm (TORA) [35]
Similar experiments were performed for a pause time fixed in 60 seconds and speed varying in 1, 4, 16, and 64 m/s, and the results show the same behavior presented in Sect. 4.2.1, i.e., as concluded for square models, the proportion between pause and speed values seems to be the major drive for the spatial distribution
Summary
Mobility keeps gaining in importance, playing a significant role in the design and implementation of communications infrastructures and distributed systems. It is important to point out that it is not the goal of this paper to defend the use of Random Waypoint and Random Direction as valid models for describing human mobility, or any other particular behavior. For those issues, the interested reader may find extensive and relevant material in [24, 30, 37]. 5 introduces one- and two-dimensional SAN descriptions for the Random Direction mobility model, discussing possible variations of these SAN models and presenting the results achieved in terms of spatial node distribution. Final remarks and some more elaborated modeling efforts (3D models) are presented in the conclusion with some possible future works
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