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首页> 外文期刊>Sensors Journal, IEEE >Adaptive Placement for Mobile Sensors in Spatial Prediction Under Locational Errors
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Adaptive Placement for Mobile Sensors in Spatial Prediction Under Locational Errors

机译：位置误差下空间预测中移动传感器的自适应位置

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摘要

This paper addresses the problem of driving robotic sensors for an energy-constrained mobile wireless network in efficiently monitoring and predicting spatial phenomena, under data locational errors. The paper first discusses how errors of mobile sensor locations affect estimating and predicting the spatial physical processes, given that spatial field to be monitored is modeled by a Gaussian process. It then proposes an optimality criterion for designing optimal sampling paths for the mobile robotic sensors given the localization uncertainties. Although the optimization problem is optimally intractable, it can be resolved by a polynomial approximation algorithm, which is proved to be practically feasible in an energy-constrained mobile sensor network. More importantly, near-optimal solutions of this navigation problem are guaranteed by a lower bound within 1-(1/e) of the optimum. The performance of the proposed approach is evaluated on simulated and real-world data sets, where impact of sensor location errors on the results is demonstrated by comparing the results with those obtained by using noise-less data locations.
机译：本文解决了在能量定位移动无线网络中驱动机器人传感器在数据位置错误下有效监视和预测空间现象的问题。本文首先讨论了移动传感器位置的误差如何影响估计和预测空间物理过程，因为要监视的空间场是通过高斯过程建模的。然后，在给定定位不确定性的情况下，提出了用于为移动机器人传感器设计最佳采样路径的最优准则。尽管优化问题是最难解决的，但是可以通过多项式逼近算法解决该问题，事实证明，该算法在能量受限的移动传感器网络中是切实可行的。更重要的是，此导航问题的接近最优的解决方案由最优值的1-（1 / e）内的下限来保证。在模拟和真实数据集上评估了所提出方法的性能，其中通过将结果与使用无噪声数据位置获得的结果进行比较，来证明传感器位置误差对结果的影响。

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