传感器网络中基于抽样的带权近似Top-k查询算法

Research on the Approximate Algorithm of Top-k Query Based on Weighted Sampling in Wireless Sensor Network

提出一种适用于传感器网络的抽样带权阀值过滤近似Top-k聚集查询算法.该近似算法会将无线传感器网络划成几个两两不相交的簇进行处理,在汇聚节点进行预处理以及在各个簇内进行抽样过滤处理,在抽样过程中给可靠而重要的节点赋上相应更大的权值,同时根据节点采集的信息具有时间相关特性,在簇内进行抽样阀值过滤处理,每个簇头节点都会接收到该簇内的Top-k候选子集,然后将每个簇的子集发送给Sink节点,该Sink节点将接收到能代表整网Top-k样本候选集.仿真实验结果显示该算法只需发送少量的数据,更小的抽样样本,并能满足任意精度要求.

An approximate algorithm of Top-k query based on sampling and weight in wireless sensor network was presented. The algorithm divides the network into several disjoint clusters in the sink node and the nodes in cluster to take sampling process. In the process of sampling, greater weight for reliable and important sensor node is given. The sensor node sensing data has a time correlation, and sampling threshold filtering in the cluster. Each cluster head node receives a Top-k candidate subset of the cluster, and then sends the subset to the sink node. Finally, the sink node can receive a Top-k sample candidate that represents the whole network. Simulation experiments show that the algorithm only needs to send small data and smaller samples, and can satisfy arbitrary precision requirements.