Research On Peer-to-Peer Layered Network Model Of Spatial Information Processing

With the rapid development of spatial information, spatial information processing hasentered the new period characterized as being more integrated, industrialized and networkedclosely with network. The existing network model of spatial information processing alreadycan't adapt to the request of distributed processing and sharing of spatial information.Meanwhile, with the development of peer-to-peer network and its comprehensive applicationin distributed system, people tend to solve large-scale scientific issues by using peer-to-peernetwork. In order to manage and maintain complex spatial information efficiently andimprove the efficiency of transmission and processing, there is an urgent need for a newnetwork model of spatial information processing. This paper deals with peer-to-peer layerednetwork model of spatial information processing(SIPPLNM) and involving technologies suchas spatial information distribution, spatial index and query, security.As some characteristics of spatial information processing and the mismatch between theoverlay and physical network in the management of the magnanimous spatial data, the paperproposes the peer-to-peer layered network model of spatial information processing(SIPPLNM), which consists of super-node layer and inner-district-node layer. Every district isin 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.According to adjacent relations and non-uniform distribution characteristics of spatialinformation, a geographic division method based on skewed Hilbert curve is presented. Byskewed Hilbert curve, the adjacent relations among objects are kept,and the balanceddistribution of spatial information among districts can be achieved. Inner the district, thebalanced distribution of spatial information in nodes of the second layer is achieved byhashing spatial information subject. Experiments show that this method can effectivelyovercome the drawbacks of existing geographic division and spatial information distributionmethods, and achieve a good storage balance in nodes of SIPPLNM. It is particularly suited tothe uneven distributed spatial data sets.Trust in distributed environment is a hot topic in network research, while current trustmanagement model can't be used into SIPPLNM directly, for there are spatial relations inspatial information processing. Corresponding to SIPPLNM, the paper develops a layer trustmodel based on subject logic theory and spatial information entity behavior, which describestrust relationships between spatial information entities. It discriminates the trust relationshipsbetween spatial information entities in the same autonomous district and super nodes indifferent districts, and handles the two different trust relationships with different approaches. By simulated experiments, it can evaluate the trust relationships between spatial informationentities precisely, thus can resolve security problems existing in P2P environment effectively.After analyzing the inherent problems in general distributed spatial index methods suchas index hotspots, access bottlenecks and the difficulty to maintain consistency of distributedspatial index structure, a distributed layer spatial indexing structure and query algorithmsbased on SIPPLNM are presented. By layer indexing structure, the entire spatial index will bedivided into a sets of district index, which helps to restrict the works of index maintaining ineach district itself as much as possible and can reduces the complexity of maintainingdistributed spatial index structure; the detail of the SIPPLNM indexing algorithms as well asrelated query algorithms are illustrated in this paper. Spatial query experiments also verify thevalidity of the SIPPLNM structure and its rapid query response performance.By analyzing practical application, SIPPLNM can process non-uniform spatialinformation efficiently and run reliably after introducing layer trust model.