Energy Saving Study On WSNs Based On The Spatial And Temporal Correlations

With the rapid development of computer technology, sensor technology and wireless communication technology, Wireless Sensor Networks (WSNs) integrates these technologies has been widely used in the field of scientific research and industrial production. Different with other traditional networks, the main purpose of WSNs is to provide effective long-term environmental automatic monitoring. Additional, sensor nodes need to periodically transmit collected data to the network for a long time. In this processing, there are amount of data and high energy of transmission. So, how to save energy through the communication of data processing is currently a hot research topic in wireless sensor networks. Meanwhile, WSNs with a wildly distribution area, the large number of nodes, there will be a greatly affected the structure of the data processing in the networks.Based on the sensor data acquisition time point and geographical position adjacent demonstrated correlation, we should to find out the model in line with the data correlation characteristics, through the model analysis and simulation platform, we design a Spatial and Temporal correlations WSNs simulation system.Firstly, Haar wavelet is based on the time correlation data compression theory, and is used for transforming data into the wavelet domain. We modeled the transformed wavelet coefficients, and proposed the High Frequency Coefficients Selecting (HFCS) algorithms. This method is effectively to removing the temporal correlation of data, but without affecting the accuracy of the data reconstruction, more conducive to reducing the amount of data transferred in the communication process, saving energy consumption, and experiments show the feasibility and superiority of the algorithm.Secondly, the spatial correlation clustering algorithm is a method for using the clustered network architecture and the concept of correlation radius. At the same time cluster heads are selected for according to the relationship between the residual energy function node distances. So, this method is effectively to gathering spatial correlation data in the cluster head, and also to avoid the cluster head being dead, then prolong the network lifetime as possible.Finally, our design is based on Spatial and Temporal correlation network simulation system, using the discrete event simulation platform OMNeT++. We design the node layers by the composite module and the reference model. We designed the network protocols by protocol stack layers, encapsulation function, C++compiler class functions and packet. The entire simulation system was built with nodes and network protocols. Through parameters and experiment data, we had verified the reliability of the simulation system, and improved the content of this paper. In this paper, our works provide a reliable basis for future research.