Query Processing Optimization Through Sample Size and Monitoring Coverage Controlling in Wireless Sensor Networks

Qingchun Ren, Q. Liang
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引用次数: 1

Abstract

Query processing has been studied extensively in traditional database systems. But few of existed methods can be directly applied to wireless sensor database systems due to their characteristics, such as decentralized nature, limited computational power, imperfect information recorded, and energy scarcity of individual sensor nodes. In this paper, we extend our previous work: quality-guaranteed and energy-efficient algorithm (QGEE) for wireless sensor database systems. We introduce radius of covering disk from point spread function (PSF) aspect and sample size for query quality and energy consumption control. PSF introduces ambiguity into query answers, since the sensitivity of nodes is nonuniform within monitoring region. Sample size determination refers to the process of determining exactly how many samples should be measured in order that the sampling distribution of estimators meets users' pre-specified target precision. In this paper, we formulate the criteria to determine the optimum radius and sample size according to users' requirements on query answers. Simulation results demonstrate that the impact of sample size and monitoring coverage on query answers in terms of root mean square error (RMSE)
基于样本大小和监测覆盖控制的无线传感器网络查询处理优化
查询处理在传统的数据库系统中得到了广泛的研究。但由于无线传感器数据库系统的分散性、计算能力的有限性、信息记录的不完全性以及单个传感器节点的能源稀缺性等特点,现有的方法很少能直接应用于无线传感器数据库系统。在本文中,我们扩展了之前的工作:无线传感器数据库系统的质量保证和节能算法(QGEE)。从点扩散函数(PSF)的角度引入覆盖磁盘半径和样本大小,用于查询质量和能耗控制。由于节点的灵敏度在监测区域内不均匀,PSF在查询答案中引入了模糊性。样本量的确定是指为了使估计量的抽样分布满足用户预先设定的目标精度,准确地确定需要测量多少个样本的过程。本文根据用户对查询答案的要求,制定了确定最优半径和样本量的准则。仿真结果表明,样本大小和监测覆盖率对查询答案的影响是均方根误差(RMSE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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