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Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

机译:无线传感器网络中基于几何的分布式空间天际线查询

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Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors’ monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption.
机译:基于无线传感器网络(WSN)的天际线查询算法已被广泛用于环境监测领域。由于监视空间位置问题的多维性质,传统的天际线查询策略会导致巨大的计算成本和能耗。为了保证传感器能量的有效利用,本文提出了一种基于几何的分布式空间查询策略(GDSSky)。首先,本文提出了一种基于几何的区域划分策略。它使用基于凸包体顶点的天际线区域缩减方法,快速查询与特定查询区域相关的空间天际线数据,并提出了一种基于三角剖分方法的区域划分策略,以在每个子区域中实现分布式查询。减少节点之间的比较时间。其次,设计了一个子区域聚类策略,可以将内部数据分组为用于并行查询的聚类,这样可以节省时间。最后,本文提出了一种基于数据节点树的分布式查询策略,以遍历所有相邻传感器的监视位置。它对已经获得和未找到的空间天际线数据进行空间天际线查询,以实现并行查询。大量的仿真结果表明,GDSSky可以快速返回更靠近查询位置且污染能力更大的地点,并显着降低WSN能耗。

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