In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributedspatial join pr...
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In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributedspatial join processing among the sensor networks. Because the distributedspatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks;our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributedspatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributedspatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes: Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.
Information in networked systems often has spatial properties: routers, sensors, or virtual machines have coordinates in a geographical or virtual space, for instance. In this paper, we propose a peer-to-peer design f...
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ISBN:
(纸本)9783901882777
Information in networked systems often has spatial properties: routers, sensors, or virtual machines have coordinates in a geographical or virtual space, for instance. In this paper, we propose a peer-to-peer design for a spatial search system that processes queries, such as range or nearest-neighbor queries, on spatial information cached on nodes inside a networked system. Key to our design is a protocol that creates a distributedindex of object locations and adapts to object and node churn. The index builds upon the concept of the minimum bounding rectangle, to efficiently encode a large set of locations. We present a search protocol, which is based on an echo protocol and performs query routing. Simulations show the efticiency of the protocol in pruning the search space, thereby reducing the protocol overhead. For many queries, the protocol efficiency increases with the network size and approaches that of an optimal protocol for large systems. The protocol overhead depends on the network topology and is lower if neighboring nodes are spatially close. As a key difference to works in spatial databases, our design is bottom-up, which makes query routing network-aware and thus efficient in networked systems.
The increasing availability of cheap location tracking devices is causing a steadily increasing demand for location based services. Such services usually utilize spatial data structures that need to scale with increas...
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ISBN:
(纸本)9780769545769
The increasing availability of cheap location tracking devices is causing a steadily increasing demand for location based services. Such services usually utilize spatial data structures that need to scale with increasing request load. While static data allows for scaling by simple service replication, dynamic data such as moving users requires administration in a single coherent system to provide consistent and up-to-date processing results. In this paper, we propose a distributed system based on a P2P architecture to store and process spatial data, in particular with window-and k-nearest-neighbors queries. Our system is very simple in that it solely manages a range-partitioned linear data space defined by a Hilbert Curve mapping and neither requires explicit hashing, clustering or the maintenance of a dedicated distributedspatial structure at all. Our main focus is on the inherent quad-tree structure of the 2d Hilbert Curve and how it suffices to efficiently evaluate nearest-neighbor queries in a distributed manner. We verify our approach using real-world data from Open Street Map and demonstrate that the throughput of our system scales asymptotically linear with the network size.
With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it's still a big challenge to manage and ...
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With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it's still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.
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