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

机译:基于地形意识的基于意识的动员模型,Markov Wark Wallation Waltical Wallation Wallation Wallation Manet

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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.
机译:地形因素可能对移动节点的动态特性产生重大影响,尤其是在战术鬃毛中。然而,大多数现有的移动性模型是概率模型,其不捕获真实的战术特征行为,例如随机干扰和旁路过程。在这项工作中,我们建立了一个正式的过程模型,以模仿小规模地形因素对节点运动的影响。在模型中,我们使用线性时间不变系统来生成带有一阶马尔可夫属性的彩色噪声,作为加速度的微小随机干扰。此外,我们提出了一种基于地形意识的旁路策略,使节点能够感知和绕过障碍区域。当节点面对湖泊和丘陵等大规模地形因素时,该策略模仿节点的反应。移动性迹线表明,我们的模型现实地提出了地形因素对节点移动性的影响。我们的过程模型中的参数可以灵活配置不同的战术场景。

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