随机行走模型下移动传感器网络的定位算法

Guey-Yun Chang, Dar-Wei Chiou
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引用次数: 1

摘要

在无线传感器网络(WSN)中,许多应用都需要位置信息来实现目标。现有的移动传感器网络定位算法大多基于序贯蒙特卡罗(SMC)方法。基于smc的定位方法在低锚点密度环境下定位误差大,通信成本高。本文提出了一种非基于smc的分布式定位方案。我们的主要思想来自于这样的观察:一个传感器节点在足够长的时间内听到另一个节点的声音,它的轨迹与被听到的节点的轨迹相似。仿真结果表明,该方案具有定位误差小、通信成本低、计算成本低等优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Localization algorithm for mobile sensor networks under the random walk model
In wireless sensor network (WSN), location information is required to achieve the goal in many applications. Most of existing localization algorithms for mobile sensor networks are based on the Sequential Monte Carlo (SMC) method. SMC-based methods has either high localization error in low-anchor density environments or high communication cost. In this paper, we propose a non-SMC-based distributed localization scheme. Our main idea comes from the observation that a sensor node which hears another node for a sufficiently long time has trajectory similar to the heard node's trajectory. Simulation shows that our scheme has low localization error, low communication cost, and low computation cost.
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