Region Sampling: Continuous Adaptive Sampling on Sensor Networks
Region Sampling: Continuous Adaptive Sampling on Sensor Networks
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DOI：
10.1109/icde.2008.4497488
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发表时间：
2008-04
期刊：
影响因子：
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通讯作者：
Song Lin;Benjamin Arai;D. Gunopulos;Gautam Das
中科院分区：
文献类型：
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作者：
Song Lin;Benjamin Arai;D. Gunopulos;Gautam Das
在满足性能要求的同时满足能量限制是部署传感器网络时的主要关注点。最近提出的许多技术为聚合近似提供了误差边界解决方案，但不能保证能量消耗。相反，我们的目标是在最小化近似误差的同时约束能量消耗。在本文中，我们提出了一种在线算法，区域采样，用于计算近似聚合，同时满足预先定义的能量预算。我们的算法通过将传感器网络分割成非重叠区域的分区，并对每个区域进行采样和局部聚合来区分。通过对采样能量成本率和采样统计数据的收集和分析，预测出最优的采样方案。在真实世界数据集上的综合实验表明，与之前提出的解决方案相比，我们的方法的准确性至少提高了10%。
Satisfying energy constraints while meeting performance requirements is a primary concern when a sensor network is being deployed. Many recent proposed techniques offer error bounding solutions for aggregate approximation but cannot guarantee energy spending. Inversely, our goal is to bound the energy consumption while minimizing the approximation error. In this paper, we propose an online algorithm, region sampling, for computing approximate aggregates while satisfying a pre-defined energy budget. Our algorithm is distinguished by segmenting a sensor network into partitions of non-overlapping regions and performing sampling and local aggregation for each region. The sampling energy cost rate and sampling statistics are collected and analyzed to predict the optimal sampling plan. Comprehensive experiments on real-world data sets indicate that our approach is at a minimum of 10% more accurate compared with the previously proposed solutions.