Research Of Skyline Query In Wireless Sensor Data

With the rapid development of the net, the traffic that the WSNs have to deal with is increasing rapidly. It is a big challenge to store and analyze huge dates now. Many IT magnates put forward cloud computing platform and cloud computing service. MapReduce is a software architecture which is put forward by Google. It is used for parallel computing for huge dates.Wireless sensor data is a kind of typical network data.The application of wireless sensor data is widely used in real word. It can be applied to environmental monitoring and forecast alarm and so on. There exists a fundamental problem in these areas, that is how to detect the critical points from huge datas faster. The article focuses on Skyline point detection according to some datas from the sensors.We care about temperature, wind and humidity in the forest fire early warning system. While we care about the speed of wind, the speed of water and the height of the wave in the ocean typhoon monitoring and warning system. We care about different attributes under different environment. It is very complicated to extract target attributes from a lot of attributes. Usually sensors across a wide range, and it is a long time to collect data. This leads to the appearance of big data. There is a high and real-time demand of WSNs, they all make Skyline queries huge network communication and big expenditure of time in big data.According to the problem how to extract target attributes from a lot of attributes. In this paper, we put forward the index to perform Skyline queries according to user preference in any subspace of wireless sensor datas. And for the problem of a big expenditure of time, in this paper, an efficient parallel algorithm for processing the Subspace Skyline Query using MapReduce is applied to wireless sensor network. This algorithm can report Skyline points in any subspace of wireless sensor network faster. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time in wireless sensor network. Experimental results indicate that our algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.