A Terrain-Awareness Based Mobility Model with Markov Random Disturbance for Tactical MANET

Abstract

Terrain factors may have significant impacts on the dynamic characteristics of mobile nodes, especially in tactical MANET. However, most of the existing mobility models are probability models, which do not catch real tactical feature behaviors such as random disturbance and bypass processes. In this work, we establish a formalized process model to imitate the impact of small-scale terrain factors on the movement of nodes. In the model, we use a linear time invariant system to generate a colored noise with first-order Markov property, as a tiny random disturbance in the acceleration. Furthermore, we propose a terrain-awareness based bypass strategy to enable the nodes to perceive and bypass the area of obstacles. This strategy imitates the reaction of nodes when the nodes face large-scale terrain factors such as lakes and hills. The mobility traces show that our model realistically presents the impacts of terrain factors on the mobility of nodes. And parameters in our process model can be configured flexibly for different tactical scenarios.