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A Connectivity Weighting DV_Hop Localization Algorithm Using Modified Artificial Bee Colony Optimization

机译:一种使用改进的人工蜜蜂殖民地优化的连接权重DV_HOP定位算法

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Node localization is a fundamental issue in wireless sensor network (WSN), as many applications depend on the precise location of the sensor nodes (SNs). Among all localization algorithms, DV_Hop is a typical range-free localization algorithm characterized by such advantages as simple realization and low energy cost. From detailed analysis of localization error for the basic DV_Hop algorithm, we propose a connectivity weighting DV_Hop localization algorithm using modified artificial bee colony optimization. Firstly, the proposed algorithm calculates the average hop distance (AHD) of anchor nodes in terms of the minimum mean squared distance error between the estimated distances of anchor nodes and the corresponding actual distances from them. After that, a connectivity weighting method, considering the influence from both local network properties of anchor nodes and the distances from anchor nodes to unknown nodes, is designed to obtain the AHD of unknown nodes. In addition, we set up the weighting calculation proportion of anchor nodes at the same time. Finally, a modified artificial bee colony algorithm which enlarges searching space is used to optimize the execution of multilateral localization. The experimental results demonstrate that the connectivity weighting approach has better localization effect, and the AHD of unknown nodes close to true value can be obtained at a relatively large probability. Moreover, the modified artificial bee colony algorithm can reduce the probability of premature convergence, and thus the localization accuracy is further improved.
机译:节点本地化是无线传感器网络(WSN)中的基本问题,因为许多应用程序取决于传感器节点(SNS)的精确位置。在所有本地化算法中,DV_HOP是一种典型的无距离定位算法,其特征在于简单的实现和低能量成本。从对基本DV_HOP算法的本地化误差进行详细分析,我们提出了一种使用改进的人工蜂殖民地优化的连接权重DV_HOP定位算法。首先,所提出的算法根据锚固节点的估计距离与来自它们的相应实际距离之间的最小平均平方距离误差来计算锚节节点的平均跳距(AHD)。之后,考虑锚点节点的局部网络属性的影响以及从锚点节点到未知节点的距离的影响,旨在获得连接性加权方法,以获得未知节点的AHD。此外,我们同时设置锚节点的加权计算比例。最后,使用了扩大搜索空间的修改的人造蜂殖民地算法来优化多边定位的执行。实验结果表明,连接权重方法具有更好的定位效果,并且可以以相对较大的概率获得接近真实值的未知节点的AHD。此外,改进的人造蜂菌落算法可以降低造成早熟的概率,因此进一步提高了本地化精度。

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