摘要
无线传感器网络的一个重要应用是可信地查询网络中所有节点的监测数据.目前,多数研究主要集中在如何利用节点之间的时空相关性,节省能量地查询感知数据.但是这些方法的查询结果不能满足某些应用对数据的高可信要求,也不能适用于节点之间不存在空间相关性或空间相关性不稳定的情况.针对这一问题,提出了基于模型拟合的可信近似查询处理方法.该方法在感知数据集合上寻找具有最小数据传输比的拟合模型,通过传输模型及其参数来代替传输实际的监测数据.理论分析和实验结果证明,基于模型拟合的可信近似查询处理方法不仅能够节省大量能源而且能够返回满足用户精度要求的可信查询结果.
With the development of communication techniques, nested computation techniques and sensor techniques, wireless sensor networks have been widely applied to many fields. They can be used for testing, sensing, collecting and processing information of monitored objects and transferring the processed information to users. Collecting data of the environments is an important application of the sensor networks. Most current researches mainly focus on querying the sensing data with low energy consumption by utilizing sensor nodes' temporal-spatial correlations. These methods can collect the data with low energy consumption, but in some scenarios their results could not satisfy the applications with high confidence about the error bounds pre-specified. Moreover, these methods are not adapted to the case that there are no spatial correlations in sensor nodes. To overcome these defaults, a new method named approximate query processing algorithm with confidence based on model fitting is proposed in this paper. The proposed method create fitting models with the lower data transfer ratio, and the models are sent back to sink node instead of sensing data themselves. The proposed method can not only return the users the data within the error bounds with low energy consumption, but also be adapted to actual sensor node for being of light-weight. Theoretical analysis and experimental results show that this method can return high confident querying results and is energy efficient.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2008年第1期73-82,共10页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展规划基金项目(2006CB303000)
国家自然科学基金重点项目(60533110)
国家自然科学基金项目(60473075)
国家教育部新世纪创新人才计划基金项目(NCET-05-0333)
黑龙江省自然科学基金重点项目(ZJG03-05)
黑龙江省青年科技专项基金项目(QC06C033)~~
关键词
传感器网络
算法
近似查询
可信
模型拟合
sensor networks
algorithm
approximate querying
confidence
model fitting