Abstract
Based on the application of wireless sensor networks, this thesis studies the sensing data compression algorithm on sensor nodes and the compressed storage processing method of massive sensor data in wireless sensor networks. Considering the spatio-temporal correlation between sensor data of a single node, an improved adaptive Huffman coding algorithm is proposed, which aims to compress the capacity of transmitted data. The algorithm is applicable to wireless sensor network nodes with limited memory and computing resources. The time-space-related sensor data is compressed in the case where the error is adjustable. And carry out the corresponding experiments and analysis. Several lossless compression algorithms for sensing data characteristics were analyzed and related comparison experiments were conducted. The results show that the algorithm can significantly reduce redundant data, have a higher compression ratio and can guarantee data reconstruction accuracy.
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