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An Improved Localization Scheme Based on PMCL Method for Large-Scale MobileWireless Aquaculture Sensor Networks

机译：大规模移动无线水产养殖传感器网络基于PMCL方法的改进定位方案

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摘要

Localization is crucial to many applications in wireless sensor networks (WSNs) because measurement data or information exchanges happened in WSNs without location information are meaningless. Most localization schemes for mobile WSNs are based on Sequential Monte Carlo (SMC) algorithm. These SMC-based methods often suffer from too many iterations, sample impoverishment and less sample diversity, which leads to low sampling and filtering efficiency, and consequently low localization accuracy and high localization costs. In this paper, we propose an improved range-free localization scheme for mobile WSNs based on improved Population Monte Carlo localization (PMCL) method, accompanying with Hidden Terminal Couple scheme. A population of probability density functions is proposed to approximate the distribution of unknown locations based on a set of observations through an iterative importance sampling procedure. Behaviors are enhanced by adopting three improved methods to increase accuracy, enhance delay and save cost. Firstly, resampling, with importance weights, is introduced in PMCL method to avoid sample degeneracy. Secondly, twofold constraints, constraining the number of random samples in initialized step and constraining valid observations in resampling step, are proposed to decrease the number of iterations. Thirdly, mixture perspective is introduced to maintain the diversity of samples in resampling weighted process. Then, localization error, delay and consumption, especial delay, are predicted based on the statistic point of view, which takes mobile model of RWP into account. Moreover, performance comparisons of PMCL with other SMC-based schemes are also proposed. Simulation results show that delay of PMCL has some superiorities to that of other schemes, and accuracy and energy consumption is improved in some cases of less anchor rate and lower mobile velocity.
机译：本地化对于无线传感器网络（WSN）中的许多应用至关重要，因为在没有位置信息的WSN中发生的测量数据或信息交换毫无意义。移动WSN的大多数本地化方案都基于顺序蒙特卡洛（SMC）算法。这些基于SMC的方法经常遭受太多的迭代，样本贫乏和较少的样本多样性，这导致较低的采样和过滤效率，从而导致较低的定位精度和较高的定位成本。在本文中，我们提出了一种基于改进的人口蒙特卡洛定位（PMCL）方法的移动WSN的改进的无范围定位方案，以及隐藏终端耦合方案。提出了一组概率密度函数，以通过迭代重要性抽样程序基于一组观察值来估计未知位置的分布。通过采用三种改进的方法来提高行为，增加延迟和节省成本，从而增强了行为。首先，在PMCL方法中引入了具有重要权重的重采样以避免样本退化。其次，提出了双重约束，即在初始化步骤中限制随机样本的数量，在重采样步骤中限制有效的观测值，以减少迭代次数。第三，引入混合透视图以保持重采样加权过程中样本的多样性。然后，基于统计的观点预测定位误差，时延和消耗，特别是时延，其中考虑了RWP的移动模型。此外，还提出了PMCL与其他基于SMC的方案的性能比较。仿真结果表明，PMCL的延迟比其他方案有一些优势，并且在锚定率较低和移动速度较低的情况下，可以提高精度和能耗。

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  • 来源
    《Arabian Journal for Science and Engineering》 |2018年第2期|1033-1052|共20页
  • 作者

    Lv Chunfeng; Zhu Jianping; Tao Zhengsu;

  • 作者单位

    Shanghai Ocean Univ, SOU Coll Engn Sci & Technol, 999 Huchenghuan Rd, Shanghai, Peoples R China;

    Shanghai Ocean Univ, SOU Coll Engn Sci & Technol, 999 Huchenghuan Rd, Shanghai, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Elect Informat & Elect Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Mobile localization; PMCL scheme; HTC algorithm; WSNs;

    机译：移动定位;PMCL方案;HTC算法;WSN;

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