An Adaptive Processing Node Selection Based Spatio-Temporal Query Alogrithm
|School||Harbin Engineering University|
|Course||Applied Computer Technology|
|Keywords||wireless sensor network Spatio-Temporal Query self-adaptive energy efficient real-time|
A wireless sensor network, which is reported as one of the most important technologies in 21st century, has become a real hot topic studied by experts. Spatio-Temporal Query is the most primary and widely used in the application of WSNs. So the technology of the Spatio-Temporal Query has become great important. WSNs exhibit special features, such as energy-constrained, communication-constrained and computation-constrained, so it is different from the traditional DataBase to perform Spatio-Temporal Query. The main difference is that Spatio-Temporal Query algorithm has to minimize energy consumption, especially select the processing node with efficient energy. But the response time is more important than reducing energy consumption in some real-time application area such as military affairs, disaster salvage and so on.On the basis of the analysis and research about the current Spatio-Temporal Query algorithms, the method that solves the problem of selecting processing node in complex environment such as the invalidation of processing node and the variety of data stream is proposed in this paper. This energy efficient method can select processing node self-adaptively. The method that solves the problem of real-time in Spatio-Temporal Query is proposed in this paper. It is a method of active transferred and real-time data aggregation. The STWin is a current framework used by Spatio-Temporal Query which is strong in decreasing energy consumption, because it uses the least nodes and the idea of processing in network. With the two new methods above, this paper presents a new algorithm-Adaptive Processing node Selection based Spatio-Temporal query algorithm, combining the STWin framework. The algorithm selects and replace processing node self-adaptively and transfer data actively for real-time data aggregation. In the end, it can reduce energy consumption and response time.Finally, the influence of node density, query region size and temporal range on energy consumption and the diversity of data on response time are studied experimentally. Theoretical and experimental results show that the algorithm proposed in this paper outperforms the existing and traditional algorithms in the STWin framework when the query region size is small compared with the whole network and the diversity of data is obvious.