Distributed algorithm for localization of large scale sensor network
Distributed algorithm for localization of large scale sensor network
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DOI：
10.1007/978-1-84628-814-2_52
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发表时间：
2006-12
期刊：
影响因子：
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通讯作者：
K. Oguni;Hiroaki Honda;K. Khor;J. Inoue
中科院分区：
文献类型：
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作者：
K. Oguni;Hiroaki Honda;K. Khor;J. Inoue
大规模传感器网络的定位（确定每个传感器节点的位置）是将网络传感技术应用于基础设施健康监测的重要问题。提出了一种水声测距的实现方法，并提出了一种分布式传感器网络定位算法。传感器节点之间的相对位置估计的基础上通过逆Delaunay算法的声学测距。该算法同时定位所有节点，从而抑制了定位误差的积累。该算法的鲁棒性和可扩展性，通过数值模拟检查。噪声容忍的声学测距算法，采用数字信号处理技术，实现在一个现成的传感器平台（Mica2）。实验表明，该算法的平均距离估计误差小于10 cm。与此声学测距算法一起，逆Delaunay算法也在Mica2中实现。这使得传感器网络的自动定位成为可能。通过野外实验对系统的定位精度进行了评估。
Localization (determination of the position of each sensor node) of a large scale sensor network is an important issue for applying network sensing technique to the health monitoring of the infrastructures. This paper presents an implementation of acoustic ranging and proposes a distributed algorithm for localization of sensor network. Relative positions between sensor nodes are estimated based on acoustic ranging through the inverse Delaunay algorithm. This algorithm localizes all the nodes simultaneously, thus, the accumulation of the error in the localization is suppressed. The robustness and the scalability of this algorithm are examined through numerical simulations. Noise tolerant acoustic ranging algorithm that employs digital signal processing techniques is implemented in an off-the-shelf sensor platform (Mica2). Experiments show that this acoustic ranging algorithm is sufficient to give average range estimation error below 10 cm. Together with this acoustic ranging algorithm, the inverse Delaunay algorithm is also implemented in Mica2. This enables automatic localization of sensor network. Field experiment was conducted to evaluate the accuracy of the localization of the system.