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An improved DV-HOP algorithm for indoor positioning based on Bacterial Foraging Optimization

机译:一种改进的基于细菌觅食优化的室内定位DV跳算法

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In order to increase the indoor positioning accuracy and reduce the positioning error in wireless sensor network, the Bacterial Foraging Optimization algorithm is introduced to improve the DV-HOP algorithm. The conventional DV-HOP algorithm is not very accurate in calculating the average distance per-hop, and it greatly affects the positioning accuracy. Hence, a new BFO-DV-HOP (Bacterial Foraging Optimization DV-HOP) algorithm is proposed in this paper. The conventional DV-Hop algorithm calculates the average distance per-hop based on the Euclidean distance and the minimum number of hops directly, and the random distribution of the irregular network topology leads to the low accuracy in average hop distance estimation. In BFO-DV-HOP algorithm, the average distance per-hop is calculated by the Bacterial Foraging Optimization algorithm (BFO) by using the minimum hops of nodes and the position information of anchor nodes. Simulation results show that 30% beacon anchors can effectively reduce the positioning error, and 10% beacon anchors can get a better performance compared with conventional algorithms.
机译:为了提高室内的定位精度,并减少在无线传感器网络的定位误差,所述细菌觅食优化算法被引入,以提高DV-HOP算法。传统的DV-HOP算法在计算每跳的平均距离非常精确,它极大地影响定位精度。因此,新的BFO-DV-HOP(细菌觅食优化DV-HOP)算法本文提出。传统的DV跳算法计算基于所述欧几里德距离,并直接跳的最小数量,并且不规则网络拓扑引线在平均跳距离估计的准确度低的随机分布的每跳的平均距离。在BFO-DV-HOP算法,每跳的平均距离是由细菌觅食优化算法(BFO)通过使用节点的最小跳数和锚节点的位置信息来计算。仿真结果表明,30%的信标锚能够有效降低定位误差,并与传统方法相比10%的信标锚可以得到更好的性能。

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