An Energy-Efficient Spatio-Temporal Query Processing Algorithm in Wireless Sensor Networks

FONT face; Verdana; LIU Liang; QIN Xiao-lin; DAI Hua; YAN Wei-zhong; PAN Jin-ji; FONT

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An Energy-Efficient Spatio-Temporal Query Processing Algorithm in Wireless Sensor Networks

更新时间：2025-07-16

- An Energy-Efficient Spatio-Temporal Query Processing Algorithm in Wireless Sensor Networks
- Acta Electronica Sinica Vol. 38, Issue 1, Pages: 54-59(2010)
- 作者机构：
  
  1. 南京航空航天大学信息科学与技术学院,江苏,南京,210016
  2. 苏州大学计算机科学与技术学院,江苏,苏州,215006
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Published：2010
- 稿件说明：
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FONT face, Verdana, LIU Liang, et al. An Energy-Efficient Spatio-Temporal Query Processing Algorithm in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2010, 38(1): 54-59.
DOI：

FONT face, Verdana, LIU Liang, et al. An Energy-Efficient Spatio-Temporal Query Processing Algorithm in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2010, 38(1): 54-59. DOI：

摘要

<FONT face=Verdana>在无线传感器网络环境中

用户经常提交的查询是时空查询，如“获得区域A在某个给定时间段内的感知数据”.由于传感器节点能量十分有限，因此，能量高效的时空查询处理是目前亟需解决的问题.首先指出了现有的时空查询处理算法能量消耗大的原因在于查询协调节点选择不合理.然后给出了理论上最优的查询协调节点的位置及其证明，并基于该理论提出了一类能量高效的传感器网络时空查询算法ECSTA.最后通过实验分析了节点密度和查询区域大小对算法能量消耗的影响.理论和实验结果表明ECSTA算法优于现有的STWin框架下的算法.

Abstract

<FONT face=Verdana>In wireless sensor networks，most of the queries submitted by users are spatio-temporal queries such as “gathering the data in region A during a specified period of time”.Since the sensor nodes have very limited power supply

energy-efficient spatio-temporal query processing is an urgent problem to be resolved at present.In this paper

we point out that the existing algorithms have high energy consuming due to unreasonable query coordinator node.Then

we derive the theoretical optimal location of the query coordinator node and propose a class of energy-efficient spatio-temporal query processing algorithms called ECSTA based on the theory.Finally

the influence of node density and query region size on energy consumption is studied experimentally.Theoretical and experimental results show that our algorithms outperform the existing algorithms in the STWin framework.

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