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Mobility prediction algorithm for mobile ad hoc network using pedestrian trajectory data

机译:基于行人轨迹数据的移动自组网移动性预测算法

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In the last years mobile ad hoc networks (MANET) has became a hot research subject. MANET is compound by mobile nodes that can be carried by people; the network formed by these nodes has then a highly changing topology. Nodes move according to people mobility patterns and a big number of the network tasks are influenced by nodes mobility. It is reasonable to think that network performance is also highly influenced by mobility. Reduce the uncertainty would have a positive impact on networks performance. In this work we propose a method to dynamically predict a pedestrian position. The method uses recursive least squares lattice adaptive filter to achieve that goal with reasonable low error.
机译:在过去的几年中,移动自组织网络(MANET)已成为研究的热点。 MANET由可以由人携带的移动节点组成;这些节点组成的网络具有高度变化的拓扑。节点根据人员移动性模式移动,并且大量网络任务受节点移动性影响。有理由认为网络性能也受到移动性的极大影响。减少不确定性将对网络性能产生积极影响。在这项工作中,我们提出了一种动态预测行人位置的方法。该方法使用递归最小二乘点阵自适应滤波器以合理的低误差实现该目标。

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