ASample: Adaptive Spatial Sampling in Wireless Sensor Networks
ASample: Adaptive Spatial Sampling in Wireless Sensor Networks
复制标题
DOI：
10.1109/sutc.2010.37
复制
发表时间：
2010-06
期刊：
影响因子：
--
通讯作者：
P. Szczytowski;Abdelmajid Khelil;N. Suri
中科院分区：
文献类型：
--
作者：
P. Szczytowski;Abdelmajid Khelil;N. Suri
无线传感器网络的一个突出应用是监测物理现象。被监测属性的值自然取决于所部署的传感器实现的空间采样的准确性。监测到的现象往往在部署前阶段具有未知的空间分布，这种分布也会随着时间的推移而变化。这可能会对监测的总体可实现准确性产生不利影响。因此，达到最优(精度驱动)的静态传感器节点部署通常是不可能的，导致空间中信号的欠采样或过采样。我们的目标是提供自适应的空间采样。关键挑战在于找出抽样过多或抽样不足的区域，并提出适当的对策。本文提出了一种基于Voronoi的自适应空间采样(ASAMPLE)方案。我们的方法从过采样区域中删除不必要的样本，并在欠采样区域中生成额外的新采样位置，以满足指定的精度要求。仿真结果表明，该方法显著有效地减小了测量精度的均方误差。
A prominent application of Wireless Sensor Networks is the monitoring of physical phenomena. The value of the monitored attributes naturally depends on the accuracy of the spatial sampling achieved by the deployed sensors. The monitored phenomena often tend to have unknown spatial distributions at pre-deployment stage, which also change over time. This can detrimentally affect the overall achievable accuracy of monitoring. Consequently, reaching an optimal (accuracy driven) static sensor node deployment is generally not possible, resulting in either under- or over-sampling of signals in space. Our goal is to provide for adaptive spatial sampling. The key challenges consist in identifying the regions of over- or under-sampling and in suggesting the appropriate countermeasures. In this paper, we propose a Voronoi based adaptive spatial sampling (ASample) solution. Our approach removes unnecessary samples from regions of over-sampling and generates additional new sampling locations in the under-sampling regions to fulfill specified accuracy requirements. Simulation results show that ASample significantly and efficiently reduces the mean square error of the achieved measurement accuracy.