Compressed Storage Processing Of The Sensor Data. Cloud Storage Environment

Because the nodes in Wireless sensor network generate massive redundant data; Data transmitting between the nodes produced a lots of challenge issues, such as a large waste of limited energy on nodes, the delay of network transmission, the difficult of massive sensor data storage and processing and so on. These problems have severely restricted the development of wireless sensor networks. For these reasons, this paper researched on sensor data compression and storage in the cloud storage environment as following:sensor data compression on node, storage and compression of massive sensor data in server.Firstly, considering time-space correlation among sensor data on a single node, this paper proposes the PCA_W algorithm, which is a combination of piecewise constant approximation and wavelet compression. It can compresse these data under adjustable error conditions. The reconstruction error, data compression ratio, consumed compression time and consumed decompression time of the algorithm are analyzed and compared with two other compression algorithms through experiments. The result shows that the PCA_W algorithm can significantly reduce redundant data with the higher compression ratio and reconstruct original data with precision guarantee.Secondly, considering that there are lots of overlap in the massive sensor data and it's difficult to store and process massive sensor data and so on, this paper proposes massive sensor data storage model based on cloud storage and a data compression method of mass sensor based on Huffman algorithm; the former enables data be accessed easier and more effective, while the latter compress massive sensor data with saving storage space and bandwidth. Experiments verify the feasibility and advantages of the posposed compression method in this paper.