Energy-Efficient Top-k Query Techniques Based on Adaptive Filters in Wireless Sensor Networks

ZHENG Ji-ping; SONG Bao-li; WANG Hai-xiang; WANG Yong-ge

doi:10.3969/j.issn.0372-2112.2015.10.034

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Energy-Efficient Top-k Query Techniques Based on Adaptive Filters in Wireless Sensor Networks

更新时间：2025-07-16

- Energy-Efficient Top-k Query Techniques Based on Adaptive Filters in Wireless Sensor Networks
- Acta Electronica Sinica Vol. 43, Issue 10, Pages: 2117-2123(2015)
- 作者机构：
  
  1. 南京航空航天大学计算机科学与技术学院,江苏,南京,210016
  2. 南京大学计算机软件新技术国家重点实验室,江苏,南京,210093
  3. 南京航空航天大学计算机科学与技术学院,江苏,南京,210016
  4. 南京大学计算机软件新技术国家重点实验室,江苏,南京,210093
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.10.034
  CLC： TP392
- Published：2015
- 稿件说明：
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ZHENG Ji-ping, SONG Bao-li, WANG Hai-xiang, et al. Energy-Efficient Top-k Query Techniques Based on Adaptive Filters in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2015, 43(10): 2117-2123.
DOI：

ZHENG Ji-ping, SONG Bao-li, WANG Hai-xiang, et al. Energy-Efficient Top-k Query Techniques Based on Adaptive Filters in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2015, 43(10): 2117-2123. DOI： 10.3969/j.issn.0372-2112.2015.10.034.

摘要

在传感器节点上安装动态窗口的过滤机制是无线传感器网络Top-

查询处理研究的一个重要方向.然而

已有过滤窗口算法会产生很大的窗口更新代价.本文针对过滤窗口更新频繁产生巨大能量消耗的问题

提出基于高斯过程回归预测的自适应滑动窗口Top-

查询处理算法FUGPR.当过滤窗口发生变化时

对传感器网络节点读数进行预测

评估窗口更新前后的代价来决定过滤窗口是否更新

从而减少了频繁更新窗口带来的巨大能量消耗.实验表明

本文提出的FUGPR算法无论在真实传感器网络环境的数据集上还是模拟的传感器网络环境数据集上都可以有效地减少由于过滤窗口更新带来的能量消耗.

Abstract

Adopting the filtering mechanism of dynamic filtering windows installed on sensor nodes to process top-

queries is an important research direction in wireless sensor networks.Existing algorithms based on filters consume a vast amount of energy on filter updating.As updating filters consume a large amount of energy

a top-

query processing algorithm adopting adaptive filters named FUGPR based on Gaussian process regression is provided.When the filters change

the sensor readings are predicted to calculate the updating costs of filters

then FUGPR decides whether the filters need to be updated or not.Thus

the energy consumption for updating filters is decreased.Experimental

results show that our approach can reduce energy consumption efficiently for updating filters on real and simulated datasets.

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