Poster abstract
Poster abstract
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海报摘要
DOI：
10.1145/2461381.2461440
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Gupta M
中科院分区：
文献类型：
--
作者：
Gupta M
空气污染数据具有长程相关性和多重分形标度等特性，可用于污染传感器节点实现节能的自适应空间采样技术。在这项工作中，我们提出了a）从去趋势波动分析的结果，以证明在塞浦路斯进行的试验收集的真实的污染数据集中存在非线性动态，B）一种新的基于多尺度最近邻的自适应空间采样（MNNASS）技术，该技术确定了可预测性，进而确定了来自不同传感器节点的数据之间的方向影响，以及c）在能量节省和测量精度方面的算法性能分析。
Air pollution data exhibit characteristics like long range correlations and multi-fractal scaling that can be exploited to implement an energy efficient, adaptive spatial sampling technique for pollution sensor nodes. In this work, we present a) results from de-trended fluctuation analysis to prove the presence of non-linear dynamics in real pollution datasets gathered from trials carried out in Cyprus, b) a novel Multi-scale Nearest Neighbors based Adaptive Spatial Sampling (MNNASS) technique that determines the predictability and in turn the directional influences between data from different sensor nodes, and c) performance analysis of the algorithm in terms of energy savings and measurement accuracy.