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Spatiotemporal wireless sensor network field approximation with multilayer perceptron artificial neural network models.

机译：多层感知器人工神经网络模型的时空无线传感器网络场近似。

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摘要

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity.;This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old.;We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of the Gulf of Maine, partitioned models show significant improved results over single global models.
机译：随着传感器在自然环境中变得越来越紧凑和可靠，在空间上分布的异构传感器网络系统稳步变得更加普及。但是，任何环境监控系统都必须考虑由于各种自然和技术原因而造成的潜在数据丢失。由于环境变量的空间不平稳性，对自然空间区域进行建模可能会出现问题，并且由于特定区域可能会受到不同空间尺度上的特定影响。这些区域内的流程之间的关系通常是短暂的，因此设计用来表示它们的模型不能保持静态。将时间因素整合到该模型中会带来进一步的复杂性。本文评估了传感器网络环境下多层感知器神经网络模型的使用，因为它们具有数据驱动的特性，其表示灵活性和直接的拟合性，因此可以解决许多此类问题处理。参数的相对重要性是通过自适应反向传播训练过程确定的，该过程收敛到用于传感平台的最佳拟合模型，以验证收集的数据或近似丢失的读数。随着条件的发展，模型将无法适应变化，因此需要训练新的模型来替代旧模型。;我们证明了MLP的准确性结果通常与空间克里金法的结果相同，但能够整合其他物理和时间参数，使其可以通过可用数据流的集合应用于任何区域。该模型的潜在用途可能不仅是估计传感器字段中丢失的数据，而且还可能标记传感器网络返回的潜在不正确，异常或非典型数据。考虑到监视现象中空间异质性的潜力，本论文进一步探讨了对空间进行分区并将单个MLP模型应用于这些分区的好处。可以设想使用空间和时间参数的神经模型系统，使得由k均值划分的时空空间由k个神经模型建模，内部权重根据每个分配区域内的主导过程分别变化。通过对缅因湾地表水流的模拟和真实数据进行评估，分区模型显示出比单个全局模型明显改善的结果。

著录项

  • 作者

    Neville, Francois.;

  • 作者单位

    The University of Maine.;

  • 授予单位 The University of Maine.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 215 p.
  • 总页数 215
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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