Realistic individual mobility Markovian models for mobile ad hoc networks

Abstract

Abstract-Mobility Models try to represent the movement behavior of devices in Mobile Ad hoc Networks. These models are used in performance evaluation of applications and communication systems, allowing the analysis of the mobility's impact. In this context, two individual mobility models for Mobile Ad hoc Networks are proposed in this paper. These models were based in [1] and intend to represent a wider movement capability. Using the proposed models it is possible to move in the same direction, in adjacent directions, to accelerate and to stop, avoiding sharp turns and sudden stops. This way, it is tried a closer representation to the real movement of the users in urban enviroments and roads. In this paper, the models are described analytically by Markov Chains. Besides that, some comparisions between the proposed models, the Waypoint model and the Random Walk model are presented through the use of simulations. *

The Impact of Simulation Area Shape on the Performance of Ad Hoc Networks

“…Random Waypoint model is the most popular mobility pattern in the entity model. However, two problems in Random Waypoint model are sharp return and sudden stop [10]. GaussMarkov mobility model, proposed by Liang and Haas [11], eliminates these problems encountered in Random Waypoint Model by varying the parameter α which defines the dependence between the current state and the previous states [12].…”

On the Verification of the Gravity Model Used for Mobility Modeling

“…where i λ β , and j µ are lagrangian multipliers associated with (2) and (3) respectively. The values of ij T which maximizes and which therefore constitute the most probable distribution of trips are the solutions of…”

“…There are basically two classes of mobility models. The entity mobility approach [1,2] simulates user behaviors on an individual basis. Although ideally it is possible to derive details from these models, the major drawback of such models is that they are simulation time intensive whenever accuracy is needed.…”

Analytical modeling of random waypoint mobility patterns

“…If the mobile node reaches the boundary of the region represented, it then changes the angle according to the incoming entry and then continues on this new path. This pattern was widely used in the last years, as in [14], [24], and [26], but it does not represent realistic movements because it generates sudden stops, sharp turns and sudden accelerations [10].…”

“…This model is considered suited to represent people and car movement. [10] presents two variations of this pattern to represent a more real movement of the users in urban environments and roads. A variation allows movements in horizontal and vertical directions and pause times during one or more time intervals.…”