Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

Yan Wang, Baoyan Song, Junlu Wang, Li Zhang, Ling Wang
<span title="2016-03-29">2016</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
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
more &raquo; ... egion 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. Sensors 2016, 16, 454 2 of 22 necessary to apply the spatial skyline query method in wireless sensor networks used for air quality monitoring [14] [15] [16] . As traditional monitoring strategies can only monitor the average situation of a large area and have limited flexibility for any small range, this paper proposes a skyline query method for sensor networks. Considering that environmental monitoring involves more spatial attributes, and often incurs a lot of computational cost for general skyline queries, we propose the geometry-based distributed spatial skyline query method in wireless sensor networks (GDSSky). The strategy can quickly find the locations which are near to the query places and have higher pollution potential, and it also can reduce the energy cost. The paper is an extension of our previous work Geometry-Based Spatial Skyline Query in Wireless Sensor Networks [17] . It adds a more detailed description, new experiments and a new distributed parallel strategy and query strategy. The major contributions of this paper are as follows: ' We design the cut of skyline region based on convex hull vertices method, which can cut out a lot of the non-skyline region by the rectangular B strategy and reduce the comparison times between nodes and improve the efficiency. Sensors 2016, 16, 454 3 of 22 Therefore, in these kinds of strategies, the dominant compare counts and computational cost will increase. The tuple is too long, so the transmission and energy consumption between data will increase. The data nodes may be obtained from severely polluted areas but far from locations crowded with people. However, these nodes are not the geographic locations people really care about. Spatial Skyline Query Method Unlike no-spatial skyline queries, the spatial skyline query strategy includes two concepts: the spatial dominance relation and the spatial skyline. ' The spatial dominance relation A data node set P = {p1, p2,..., pn} representing the sensor nodes includes some points in 2-dimensional space. Among them, the function of the sensor node is sensing data, transforming data and participating in the skyline query. The query node set Q = {q1,...,qn} shown as the residential areas in Figure 1 includes some points in 2-dimensional space. D(pi, qj) is the distance in 2-dimensional space. The node dominance relation is shown in Figure 1 . Among them, rectangle B is the minimum rectangle boundary MBR. In the query process, B updates constantly, B_ {new} = BXMBR (SR (p, Q)). The interior of B contains all the candidate nodes. Nodes outside B are the dominated ones, so there is no judgment of dominance relation to those. Sensors 2016, 16, 454 3 of 21 Therefore, in these kinds of strategies, the dominant compare counts and computational cost will increase. The tuple is too long, so the transmission and energy consumption between data will increase. The data nodes may be obtained from severely polluted areas but far from locations crowded with people. However, these nodes are not the geographic locations people really care about. Spatial Skyline Query Method Unlike no-spatial skyline queries, the spatial skyline query strategy includes two concepts: the spatial dominance relation and the spatial skyline.  The spatial dominance relation A data node set P = {p1, p2,..., pn} representing the sensor nodes includes some points in 2dimensional space. Among them, the function of the sensor node is sensing data, transforming data and participating in the skyline query. The query node set Q = {q1,...,qn} shown as the residential areas in Figure 1 includes some points in 2-dimensional space. D(pi, qj) is the distance in 2dimensional space. The node dominance relation is shown in Figure 1 . Among them, rectangle B is the minimum rectangle boundary MBR. In the query process, B updates constantly, B_ {new} = B∩MBR (SR (p, Q)). The interior of B contains all the candidate nodes. Nodes outside B are the dominated ones, so there is no judgment of dominance relation to those.
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