空间信息的P2P分层网络模型研究

Research on Peer-to-Peer layered network model of spatial information

针对空间信息的非均匀分布和邻近性特点,以及海量空间信息处理中逻辑覆盖网络与物理网络不一致的问题,引入对等网络(P2P)分层理论和非均匀Hilbert曲线,提出一种适合空间信息处理的P2P分层网络模型(SIPLNM).该模型分为两层:超级节点层和区域节点层,超级节点层是由负责相应区域的超级节点组成的,区域节点层由一个区域内的所有节点组成.通过非均匀Hilbert曲线保持空间对象之间的邻近性,实现空间信息在划分区域之间的均匀分布.区域内采用hash空间信息主题方式,实现空间信息在第二层节点的均衡分布.实验表明,本方法能够有效地克服现有区域划分和空间信息分布方法的不足,在SIPLNM各节点中,均有良好的分布均衡性.

Aiming at the skewed distribution characteristics and adjacent relations of spatial information and the mismatch between the overlay and physical network in the management of the magnanimous spatial data,we proposed the peer-to-peer layered network model of spatial information processing（SIPLNM） based on the Peer-to-Peer（P2P） layered theory and skewed Hilbert curve,which consists of super-node layer and inner-district-node layer.Every district is in charged of one super node in the district,and the super-node layer consists of super nodes;the inner-district-node layer consists of all nodes of one district.In this model,the adjacent relations and the balanced distribution among districts can be achieved by the skewed Hilbert curve.In the district,the balanced distribution of spatial information in nodes of the second layer is achieved by hashing spatial information subject.The experiment results show that this method can effectively overcome the drawbacks of existing geographic division and spatial information declustering methods,and achieve a good storage balance in nodes of SIPLNM.