本文選題：無線傳感器網(wǎng)絡(luò) + 壓縮感知�。� 參考：《上海交通大學》2014年博士論文

【摘要】：無線傳感器網(wǎng)絡(luò)是以數(shù)據(jù)為中心的網(wǎng)絡(luò),其主要目的是從監(jiān)測區(qū)域內(nèi)收集感知對象的信息,并對其進行處理,以盡可能少的能耗傳輸?shù)綌?shù)據(jù)管理中心。然而,無線傳感器網(wǎng)絡(luò)節(jié)點數(shù)量眾多,分布密集,節(jié)點資源(包括能量、通信、計算和存儲能力)受限,如何對感知數(shù)據(jù)進行處理及高效傳輸是無線傳感器網(wǎng)絡(luò)中的核心問題。針對無線傳感器網(wǎng)絡(luò)自身獨特的特點及傳統(tǒng)傳感信息處理與傳輸方法的不足,本文提出了基于壓縮感知的傳感信息處理與傳輸機制,不僅簡化了節(jié)點信息處理的復雜度,降低了對計算資源的要求,而且克服了傳統(tǒng)壓縮算法中信息處理的不對稱性。本文根據(jù)無線傳感器網(wǎng)絡(luò)中的不同應用需求,以提高網(wǎng)絡(luò)容量、降低數(shù)據(jù)傳輸時延以及減少整個網(wǎng)絡(luò)的傳輸能耗為設(shè)計目標,著重從傳感數(shù)據(jù)采樣方式、網(wǎng)絡(luò)路由協(xié)議設(shè)計、節(jié)點調(diào)度策略設(shè)計及網(wǎng)絡(luò)性能分析等幾個方面進行深入研究,探討一種無線傳感器網(wǎng)絡(luò)的信息處理與傳輸?shù)男聶C制。本文的主要研究內(nèi)容概括如下:1.基于壓縮感知的大規(guī)模無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)收集本文首先將壓縮感知理論引入大規(guī)模無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)收集應用中,研究了單匯聚節(jié)點和多匯聚節(jié)點的數(shù)據(jù)收集網(wǎng)絡(luò)的網(wǎng)絡(luò)容量及傳輸時延問題。針對單匯聚節(jié)點的數(shù)據(jù)收集網(wǎng)絡(luò),給出了基于壓縮感知框架下數(shù)據(jù)收集網(wǎng)絡(luò)的網(wǎng)絡(luò)容量上界,提出了一種最優(yōu)的網(wǎng)絡(luò)容量下界的路由與調(diào)度策略,分析了數(shù)據(jù)傳輸時延性能。針對多匯聚節(jié)點的數(shù)據(jù)收集網(wǎng)絡(luò),首次引入了稀疏隨機投影理論,提出了基于壓縮感知的多會話數(shù)據(jù)收集方法;給出了多匯聚節(jié)點的數(shù)據(jù)收集網(wǎng)絡(luò)的網(wǎng)絡(luò)容量上界,構(gòu)造了一種多會話生成樹并提出了最優(yōu)的網(wǎng)絡(luò)容量下界的路由與調(diào)度策略,分析了數(shù)據(jù)傳輸時延性能。理論分析及仿真結(jié)果表明,壓縮感知方法能大大提高大規(guī)模無線傳感器數(shù)據(jù)收集網(wǎng)絡(luò)的網(wǎng)絡(luò)容量及降低數(shù)據(jù)傳輸時延。2.基于壓縮感知的的網(wǎng)間計算基于壓縮感知的數(shù)據(jù)傳輸在傳輸過程中通過將節(jié)點間的數(shù)據(jù)轉(zhuǎn)發(fā)轉(zhuǎn)化為節(jié)點間的數(shù)據(jù)計算,從而降低整個網(wǎng)絡(luò)的傳輸能耗。因此,基于壓縮感知的數(shù)據(jù)收集方法對降低無線傳感器網(wǎng)絡(luò)的傳輸能耗到底帶來多大的優(yōu)勢是一個值得研究的課題。本文將壓縮感知理論中對隨機投影的構(gòu)造轉(zhuǎn)化為對一個多輪隨機線性目標函數(shù)的計算,提出了基于樹結(jié)構(gòu)以及基于流言的計算協(xié)議來實現(xiàn)基于壓縮感知的網(wǎng)間計算,首次從網(wǎng)間計算的角度評估壓縮感知在傳輸能耗及傳輸時延上的性能表現(xiàn)。針對基于樹結(jié)構(gòu)的計算協(xié)議,提出了在最優(yōu)的計算更新速率下的路由及調(diào)度策略,以及考慮傳感數(shù)據(jù)的時間相關(guān)性時用于進一步提高計算性能的塊計算協(xié)議。針對基于流言的計算協(xié)議,提出了一種廣播流言算法,以實現(xiàn)網(wǎng)絡(luò)拓撲易變情況下的信息傳輸。理論分析及仿真結(jié)果表明,本文提出的基于壓縮感知的計算協(xié)議能有效減少網(wǎng)絡(luò)的傳輸能耗及降低數(shù)據(jù)傳輸時延。3.基于壓縮感知理論及隨機游走的數(shù)據(jù)收集本文在壓縮感知理論基礎(chǔ)上,提出了一種基于隨機游走的無線傳感器網(wǎng)絡(luò)數(shù)據(jù)收集算法。首次從圖論、馬爾可夫鏈理論及壓縮感知理論等理論角度研究了該算法可行性的理論依據(jù),給出了隨機游走路徑步長及所需的隨機游走路徑數(shù)等重要參數(shù),并分析了基于?1范數(shù)最小化算法進行信號重構(gòu)的理論依據(jù)。該算法突破了傳統(tǒng)壓縮感知理論中節(jié)點需均勻采樣的限制,為壓縮感知理論在無線傳感器網(wǎng)絡(luò)中的應用提供了一種更切實可行的方法;與基于傳統(tǒng)壓縮感知理論的收集方法相比,具有占用存儲空間小、計算復雜度低以及傳輸能耗低等優(yōu)點。
[Abstract]:Wireless sensor networks (WSN) is a data centric network. The main purpose of the network is to collect and process the information of the perceived objects from the monitoring area, and to transmit it to the data management center with as little energy as possible. However, the nodes of the wireless sensor network are large and dense, and the node resources (including energy, communication, computing and storage) are dense. The core problem of wireless sensor networks is how to handle and transmit the perceptual data efficiently. In view of the unique characteristics of the wireless sensor network and the shortage of traditional sensing information processing and transmission methods, this paper proposes a sensing information processing and transmission mechanism based on compressed sensing, which not only simplifies the node letter. The complexity of interest processing reduces the demand for computing resources and overcomes the asymmetry of information processing in traditional compression algorithms. According to the different application requirements in wireless sensor networks, this paper aims to improve the network capacity, reduce the delay of data transmission and reduce the transmission energy consumption of the entire network. According to the methods of sampling, network routing protocol design, node scheduling strategy design and network performance analysis, a new mechanism of information processing and transmission of wireless sensor networks is discussed. The main contents of this paper are summarized as follows: 1. data collection of large-scale wireless sensor networks based on compressed sensing In this paper, the compression perception theory is introduced into the data collection application of large-scale wireless sensor networks. The network capacity and transmission delay of the data collection network with single aggregation nodes and multiple converging nodes are studied. The network of data collection network based on the compressed sensing framework is given for the data collection network of single aggregation nodes. An optimal routing and scheduling strategy for the lower bound of network capacity is proposed. The performance of data transmission delay is analyzed. The sparse random projection theory is introduced for the first time in the data collection network of multi aggregation nodes, and a multi session data collection method based on compressed sensing is proposed, and the data collection of multiple aggregation nodes is given. In the upper bound of network capacity, a multi session generation tree is constructed and the optimal routing and scheduling strategy of the network capacity lower bound is proposed. The performance of data transmission delay is analyzed. The theoretical analysis and simulation results show that the compressed sensing method can greatly improve the network capacity and reduce the data of the large scale wireless sensor data collection network. Transmission delay.2. based on compressed sensing based inter network computing, data transmission based on compressed sensing is converted into data computing by transferring data between nodes in the transmission process, thus reducing the energy consumption of the entire network. Therefore, the data collection method based on compressed sensing is used to reduce the transmission energy of Wireless Sensor Networks. In this paper, this paper transforms the construction of random projection into a multi wheel random linear objective function in the compression perception theory, and proposes a algorithm based on tree structure and a rumor based computing protocol to compute the Internet based on compressed sensing. The performance of compressed sensing on transmission energy consumption and transmission delay is evaluated. The routing and scheduling strategy at the optimal computing update rate are proposed for computing protocol based on tree structure, as well as the block computing protocol, which is used to further improve the computing performance when the temporal correlation of sensing data is considered. A broadcast gossip algorithm is proposed to achieve information transmission in a network topology. The theoretical analysis and simulation results show that the proposed compression based computing protocol can effectively reduce network transmission energy consumption and reduce data transmission delay.3. based on compressed sensing theory and random walk data collection. In this paper, based on the theory of compressed sensing, this paper presents a data collection algorithm for wireless sensor networks based on random walk. The theoretical basis of the feasibility of this algorithm is studied from the theory of graph theory, Markov chain theory and compression perception theory. The steps of random walking path and the number of random walk paths are given. The theoretical basis of signal reconstruction based on the 1 norm minimization algorithm is analyzed. The algorithm breaks through the restriction of uniform sampling in the traditional compressed sensing theory, and provides a more practical method for the application of compressed sensing theory to wireless sensor networks; and based on the traditional compression perception theory. Compared with the collection method, it has the advantages of small storage space, low computational complexity and low transmission energy consumption.

【學位授予單位】：上海交通大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5

【共引文獻】

相關(guān)期刊論文 前10條

1 王蓉芳;焦李成;劉芳;楊淑媛;;利用紋理信息的圖像分塊自適應壓縮感知[J];電子學報;2013年08期

2 張冰塵;戴博偉;;一種基于隨機濾波的神經(jīng)動作電位信號壓縮感知采樣方法[J];電子與信息學報;2013年09期

3 王麗艷;韋志輝;;低劑量CT的線性Bregman迭代重建算法[J];電子與信息學報;2013年10期

4 陳潔;薄遵望;韓申生;;高斯振幅調(diào)制下基于稀疏性的鬼成像[J];光學學報;2013年09期

5 CAI Yun;LI Song;;Compressed data separation via dual frames based split-analysis with Weibull matrices[J];Applied Mathematics:A Journal of Chinese Universities(Series B);2013年04期

6 李恩榮;陳明亮;龔文林;喻虹;韓申生;;鬼成像系統(tǒng)的互信息[J];光學學報;2013年12期

7 李然;干宗良;崔子冠;朱秀昌;;壓縮感知圖像重建算法的研究現(xiàn)狀及其展望[J];電視技術(shù);2013年19期

8 蔣國良;馬永濤;趙宇;;基于稀疏信號結(jié)構(gòu)信息的壓縮檢測算法[J];電子產(chǎn)品世界;2014年01期

9 孫虎;;利用ZC序列的OFDM同步及稀疏信道估計[J];電子科技;2013年11期

10 項鳳濤;王正志;袁興生;;基于壓縮感知原理的融合判別信息的協(xié)作表示方法[J];國防科技大學學報;2013年05期

相關(guān)博士學位論文 前10條

1 吳宣夠;基于壓縮感知的大規(guī)模無線傳感器網(wǎng)數(shù)據(jù)收集研究[D];中國科學技術(shù)大學;2013年

2 劉小林;多天線場景下多媒體傳輸系統(tǒng)的研究[D];中國科學技術(shù)大學;2013年

3 查長軍;分布式壓縮感知及輪廓識別研究[D];安徽大學;2013年

4 丁昕苗;基于多示例學習的恐怖視頻識別技術(shù)研究[D];中國礦業(yè)大學（北京）;2013年

5 呂偉;MIMO無線通信系統(tǒng)中的稀疏信號檢測與優(yōu)化[D];華中科技大學;2013年

6 王法松;盲源分離的擴展模型與算法研究[D];西安電子科技大學;2013年

7 宋相法;基于稀疏表示和集成學習的若干分類問題研究[D];西安電子科技大學;2013年

8 李彥兵;基于微多普勒效應的運動車輛目標分類研究[D];西安電子科技大學;2013年

9 張選德;基于非局部信息的圖像恢復和圖像質(zhì)量評價[D];西安電子科技大學;2013年

10 李志雄;大型船舶推進系統(tǒng)的動力學建模與狀態(tài)監(jiān)測方法研究[D];武漢理工大學;2013年

相關(guān)碩士學位論文 前10條

1 李建偉;冗余字典在數(shù)字水印圖像中的應用[D];北方工業(yè)大學;2013年

2 陳致豪;基于稀疏表示與壓縮傳感的超分辨率圖像處理技術(shù)研究[D];西南交通大學;2013年

3 宋騰;分數(shù)階Fourier域的圖像壓縮感知研究[D];鄭州大學;2013年

4 謝貞輝;基于壓縮感知的嵌入式圖像采集節(jié)點的設(shè)計與實現(xiàn)[D];安徽大學;2013年

5 仇樂樂;無線傳感網(wǎng)中基于量化壓縮感知的圖像傳輸方法研究[D];安徽大學;2013年

6 文首先;壓縮感知匹配追蹤算法的研究[D];安徽大學;2013年

7 蔡霞;基于傳感網(wǎng)絡(luò)的分布式壓縮采樣研究[D];天津理工大學;2013年

8 段世芳;壓縮感知中的圖像重構(gòu)算法研究[D];天津理工大學;2013年

9 郭凱;模擬信號壓縮采樣的研究[D];天津理工大學;2013年

10 張旭坤;壓縮感知的率失真性能分析研究[D];天津理工大學;2013年

，

本文編號：1840058