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一种改进的节点定位方法 被引量：1

AN IMPROVED NODE LOCALISATION ALGORITHM
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摘要 针对无线传感器网络(WSN)的节点定位问题,提出一种将蚁群算法和蒙特卡罗相结合的算法AC-MCL(Ant Colony–Monte Carlo Location):利用蚁群算法节点的分布式概率和转移概率,对节点进行位置预测,从而实现节点定位。由于蒙特卡罗算法的引入,消除了定位至少需要三个锚节点的局限性。仿真实验结果表明,该算法在规模较大而锚节点比例低的情况下仍然能够对未知节点进行准确定位,且定位的精度更高。 Aiming at the issue of node location in wireless sensor network （ WSN）, an algorithm combining the ant colony and Monte Carlo algorithms is proposed in this paper. It uses the distribution probability and the transition probability of the ant colony algorithm to predict the localisation of nodes so as to achieve the localisation of the nodes. Moreover, the limitation of the localisation that needs at least three nodes is eliminated due to the introduction of Monte Carlo algorithm. Simulation experimental results show that this method is still able to achieve accurate localisation for unknown nodes when the number of total nodes is large but with a low rate of anchor nodes, and the accuracy is even higher.
作者 王培东 祁春莉
机构地区 哈尔滨理工大学计算机科学与技术学院
出处 《计算机应用与软件》 CSCD 北大核心 2012年第8期242-244,268,共4页 Computer Applications and Software
关键词 无线传感器网络 分布式概率 转移概率 位置预测 锚节点 Wireless sensor network（WSN） Distribution probability Transition probability Location prediction Anchor node
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献11

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二级参考文献23

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计算机应用与软件
2012年 第8期

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