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Node localization using mobile robots in delay-tolerant sensor networks

机译:容错传感器网络中使用移动机器人进行节点定位

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摘要

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.
机译:我们提出了一种在延迟容忍传感器网络(DTN)中进行节点定位的新颖方案。在DTN中,传感器设备通常组织在可能相互断开连接的网络群集中。一些移动机器人可能被用来从网络集群中收集数据。我们方案中的关键思想是使用该机器人根据从它们接收到的无线电消息的信号强度,对它经过的传感器节点执行位置估计。因此,我们消除了静态传感器节点的处理约束,并消除了对静态参考信标的需求。我们的数学贡献是使用基于鲁棒扩展卡尔曼滤波器(REKF)的状态估计器来解决定位问题。与标准扩展卡尔曼滤波器相比,REKF的计算效率更高,并且更强大。最后,我们在混合传感器网络测试台上实现了我们的定位方案,并表明在大型室内环境中它可以在1 m内达到节点定位精度。

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