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一种基于遗传算法的无线传感器网络改进定位算法。

An improved localization algorithm based on genetic algorithm in wireless sensor networks.

作者信息

Peng Bo, Li Lei

机构信息

Graduate School of Engineering, Hosei University, Koganei, Tokyo 184-8584 Japan.

Faculty of Science and Engineering, Hosei University, Tokyo, Koganei 184-8584 Japan.

出版信息

Cogn Neurodyn. 2015 Apr;9(2):249-56. doi: 10.1007/s11571-014-9324-y. Epub 2015 Jan 18.

Abstract

Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

摘要

无线传感器网络(WSN)在许多应用中得到了广泛应用。WSN是一种由节点组成的无线分散结构网络,这些节点可自主建立网络。节点定位,即确定节点在网络中的位置,是许多传感器网络操作和应用的重要组成部分。现有的定位算法可分为两类:基于距离的和无需测距的。基于距离的定位算法对硬件有要求,因此在实际中实施成本较高。无需测距的定位算法降低了硬件成本。由于WSN设备的硬件限制,无需测距的定位解决方案正被视为一种比更昂贵的基于距离的方法更具成本效益的替代方案。然而,与基于距离的算法相比,这些技术通常具有更高的定位误差。DV-Hop是一种利用跳距估计的典型无需测距的定位算法。在本文中,我们提出了一种基于遗传算法的改进DV-Hop算法。仿真结果表明,我们提出的算法与以前的算法相比提高了定位精度。

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本文引用的文献

1
Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems.
IEEE Trans Syst Man Cybern B Cybern. 1998;28(5):629-40. doi: 10.1109/3477.718514.

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