本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 數(shù)據(jù)融合��； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：作為物聯(lián)網(wǎng)的底層技術(shù)支撐，無(wú)線傳感器網(wǎng)絡(luò)采集和傳輸監(jiān)測(cè)區(qū)域的各種信息，，為軍事國(guó)防、遠(yuǎn)程醫(yī)療和環(huán)境監(jiān)測(cè)等應(yīng)用提供實(shí)時(shí)可靠的數(shù)據(jù)，是物聯(lián)網(wǎng)的關(guān)鍵信息傳輸技術(shù)。但是，無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)分布密集并且采樣頻繁。感知數(shù)據(jù)之間的時(shí)空相關(guān)性造成冗余數(shù)據(jù)。大量冗余數(shù)據(jù)的傳輸對(duì)無(wú)線傳感器網(wǎng)絡(luò)有限的能量、存儲(chǔ)能力和網(wǎng)絡(luò)帶寬帶來(lái)了巨大的挑戰(zhàn)。數(shù)據(jù)融合能有效地減少冗余數(shù)據(jù)，提高數(shù)據(jù)收集效率和準(zhǔn)確性。因此，無(wú)線傳感器網(wǎng)絡(luò)數(shù)據(jù)融合研究具有重要的學(xué)術(shù)意義和工程價(jià)值。 本文深入地研究了典型的數(shù)據(jù)融合算法的原理、特點(diǎn)和性能指標(biāo)，結(jié)合單個(gè)無(wú)線傳感器網(wǎng)絡(luò)感知數(shù)據(jù)的時(shí)間相關(guān)性和多個(gè)無(wú)線傳感器網(wǎng)絡(luò)感知數(shù)據(jù)的空間相關(guān)性，提出時(shí)空相關(guān)性數(shù)據(jù)融合算法的架構(gòu)，設(shè)計(jì)了兩種高效的數(shù)據(jù)融合算法。 本文主要的研究?jī)?nèi)容有如下幾點(diǎn)： ①針對(duì)單個(gè)無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)感知數(shù)據(jù)冗余度高的問(wèn)題，本文提出了一種基于時(shí)間相關(guān)性的數(shù)據(jù)融合算法。在分段一元線性回歸模型的基礎(chǔ)上，通過(guò)對(duì)無(wú)線傳感器網(wǎng)絡(luò)感知數(shù)據(jù)的時(shí)間序列進(jìn)行分析，建立預(yù)測(cè)模型，并根據(jù)該模型的各個(gè)參數(shù)和給定的誤差，自適應(yīng)地調(diào)整下一個(gè)采集時(shí)間，并動(dòng)態(tài)地優(yōu)化回歸模型。仿真和實(shí)驗(yàn)結(jié)果證明，針對(duì)不同的數(shù)據(jù)變化率，該算法均能減少數(shù)據(jù)采集量和傳輸量，滿足數(shù)據(jù)精度。 ②針對(duì)多個(gè)無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)感知數(shù)據(jù)冗余度高的問(wèn)題，本文提出一種基于空間相關(guān)性的數(shù)據(jù)融合算法。該算法將監(jiān)測(cè)區(qū)域根據(jù)無(wú)線傳感器網(wǎng)絡(luò)感知數(shù)據(jù)的空間相關(guān)性的強(qiáng)弱程度劃分為若干個(gè)相關(guān)區(qū)域。每個(gè)相關(guān)區(qū)域根據(jù)無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)的剩余能量，選取一個(gè)代表節(jié)點(diǎn)。多個(gè)相關(guān)區(qū)域根據(jù)代表節(jié)點(diǎn)的剩余能量和到匯聚節(jié)點(diǎn)的距離，選取一個(gè)簇頭節(jié)點(diǎn)。代表節(jié)點(diǎn)將相關(guān)區(qū)域的感知數(shù)據(jù)融合后傳遞給簇頭節(jié)點(diǎn)。簇頭節(jié)點(diǎn)將數(shù)據(jù)通過(guò)無(wú)線方式轉(zhuǎn)發(fā)給匯聚節(jié)點(diǎn)。仿真和實(shí)驗(yàn)結(jié)果證明，該算法使得代表節(jié)點(diǎn)和簇頭節(jié)點(diǎn)分布均勻，數(shù)據(jù)傳輸量小，數(shù)據(jù)精度高。該算法具有很好的能效性。
[Abstract]:As the underlying technology support of the Internet of things, wireless sensor network (WSN) collects and transmits various kinds of information in the monitoring area, which provides real-time and reliable data for military defense, telemedicine and environmental monitoring applications. It is the key information transmission technology in the Internet of things. However, wireless sensor network nodes are densely distributed and frequently sampled. The temporal and spatial correlation between perceptual data results in redundant data. The transmission of large amounts of redundant data brings great challenges to the limited energy, storage capacity and bandwidth of wireless sensor networks. Data fusion can effectively reduce redundant data and improve the efficiency and accuracy of data collection. Therefore, the research of wireless sensor network data fusion has important academic significance and engineering value. In this paper, the principle, characteristics and performance index of typical data fusion algorithm are deeply studied, and the temporal correlation of sensing data of single wireless sensor network and the spatial correlation of sensing data of multiple wireless sensor networks are combined. The framework of spatio-temporal correlation data fusion algorithm is proposed, and two efficient data fusion algorithms are designed. The main contents of this paper are as follows: In order to solve the problem of high data redundancy in a single wireless sensor network node, this paper proposes a data fusion algorithm based on time correlation. Based on the piecewise univariate linear regression model, the prediction model is established by analyzing the time series of sensor network perceptual data, and according to each parameter and given error of the model, The next acquisition time is adjusted adaptively and the regression model is dynamically optimized. The simulation and experimental results show that the algorithm can reduce the amount of data acquisition and transmission and meet the accuracy of the data. In order to solve the problem of high data redundancy of multiple nodes in wireless sensor networks, this paper proposes a data fusion algorithm based on spatial correlation. The algorithm divides the monitoring region into several related regions according to the spatial correlation degree of the sensor network perceptual data. According to the residual energy of wireless sensor network nodes, each related region selects a representative node. A cluster head node is selected according to the residual energy of the node and the distance from the node to the convergence node. The representative node fuses the perceptual data of the related region and passes it to the cluster head node. The cluster head node forwards the data to the convergent node by wireless way. The simulation and experimental results show that the algorithm makes the representative node and cluster head node distribute uniformly, the data transmission is small, and the data precision is high. The algorithm has good energy efficiency.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5;TP212.9

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