本文選題：傳感數(shù)據(jù) + 壓縮算法　； 參考：《南京理工大學(xué)》2012年碩士論文

【摘要】：無(wú)線(xiàn)傳感器網(wǎng)絡(luò)節(jié)點(diǎn)生成大量冗余數(shù)據(jù),這些數(shù)據(jù)在節(jié)點(diǎn)間的轉(zhuǎn)發(fā)會(huì)引發(fā)一系列問(wèn)題,如節(jié)點(diǎn)上有限能量的大量浪費(fèi)、網(wǎng)絡(luò)傳輸延遲、網(wǎng)絡(luò)中海量傳感數(shù)據(jù)存儲(chǔ)處理困難等。這些問(wèn)題都嚴(yán)重制約了無(wú)線(xiàn)傳感器網(wǎng)絡(luò)應(yīng)用的進(jìn)一步發(fā)展和大規(guī)模推廣使用。針對(duì)這些問(wèn)題,本文從以下兩個(gè)方面來(lái)進(jìn)行云存儲(chǔ)環(huán)境中傳感數(shù)據(jù)壓縮存儲(chǔ)研究：傳感器節(jié)點(diǎn)數(shù)據(jù)壓縮和海量傳感數(shù)據(jù)壓縮存儲(chǔ)。 一、考慮到單個(gè)節(jié)點(diǎn)傳感數(shù)據(jù)間存在時(shí)空相關(guān)性,本文提出了分段常量近似與小波壓縮相結(jié)合的PCA_W壓縮算法,在誤差可調(diào)的情況下針對(duì)該類(lèi)時(shí)空相關(guān)的傳感數(shù)據(jù)進(jìn)行壓縮處理。實(shí)驗(yàn)分析比較了PCA_W算法與其它兩種壓縮算法在數(shù)據(jù)重構(gòu)誤差、數(shù)據(jù)壓縮比、壓縮耗時(shí)和解壓耗時(shí)方面的表現(xiàn)。結(jié)果表明PCA W算法可以顯著減少冗余數(shù)據(jù),有較高的壓縮比并可以保證數(shù)據(jù)重構(gòu)精度。 二、考慮到網(wǎng)絡(luò)中海量傳感數(shù)據(jù)存在大量重疊、存儲(chǔ)處理困難等問(wèn)題,本文提出基于云存儲(chǔ)的海量傳感數(shù)據(jù)存儲(chǔ)模型,使得上層應(yīng)用數(shù)據(jù)存取訪(fǎng)問(wèn)更便捷、更有效；設(shè)計(jì)基于哈夫曼算法的海量傳感數(shù)據(jù)壓縮的方法,壓縮海量傳感數(shù)據(jù),可以節(jié)約存儲(chǔ)空間和帶寬。實(shí)驗(yàn)驗(yàn)證了本文所提出的壓縮方法在壓縮比、壓縮處理耗時(shí)以及解壓處理耗時(shí)方面具有的優(yōu)勢(shì)。
[Abstract]:Wireless sensor network nodes generate a large number of redundant data, which will lead to a series of problems among nodes, such as a large amount of waste of limited energy on the nodes, network transmission delay, and the difficulty of storing and processing massive sensor data in the network. These problems have seriously restricted the further development of wireless sensor network applications and large-scale popularization. In order to solve these problems, this paper studies sensor data compression storage in cloud storage environment from the following two aspects: sensor node data compression and mass sensor data compression storage. Firstly, considering the spatio-temporal correlation between sensor data of single node, a PCAW compression algorithm combining piecewise constant approximation and wavelet compression is proposed, which can compress the sensing data with adjustable error. The performance of PCAW algorithm and other two compression algorithms in data reconstruction error, data compression ratio, compression time and decompression time are analyzed and compared experimentally. The results show that PCA W algorithm can significantly reduce the redundant data, have a high compression ratio and ensure the accuracy of data reconstruction. Second, considering that there is a lot of overlap in the massive sensing data in the network, the storage model of the mass sensing data based on cloud storage is proposed in this paper, which makes the access to the upper application data more convenient and effective. The method of mass sensing data compression based on Huffman algorithm is designed. The storage space and bandwidth can be saved by compressing the mass sensing data. Experimental results show that the proposed compression method has advantages in compression ratio, compression processing time and decompression processing time.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP333

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本文編號(hào)：2088576