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

【摘要】：無(wú)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(cè)區(qū)域內(nèi)收集感知對(duì)象的信息,并對(duì)其進(jìn)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(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)數(shù)量眾多,分布密集,節(jié)點(diǎn)資源(包括能量、通信、計(jì)算和存儲(chǔ)能力)受限,如何對(duì)感知數(shù)據(jù)進(jìn)行處理及高效傳輸是無(wú)線傳感器網(wǎng)絡(luò)中的核心問(wèn)題。針對(duì)無(wú)線傳感器網(wǎng)絡(luò)自身獨(dú)特的特點(diǎn)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(jī)制,不僅簡(jiǎn)化了節(jié)點(diǎn)信息處理的復(fù)雜度,降低了對(duì)計(jì)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(duì)稱性。本文根據(jù)無(wú)線傳感器網(wǎng)絡(luò)中的不同應(yīng)用需求,以提高網(wǎng)絡(luò)容量、降低數(shù)據(jù)傳輸時(shí)延以及減少整個(gè)網(wǎng)絡(luò)的傳輸能耗為設(shè)計(jì)目標(biāo),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(jì)、節(jié)點(diǎn)調(diào)度策略設(shè)計(jì)及網(wǎng)絡(luò)性能分析等幾個(gè)方面進(jìn)行深入研究,探討一種無(wú)線傳感器網(wǎng)絡(luò)的信息處理與傳輸?shù)男聶C(jī)制。本文的主要研究?jī)?nèi)容概括如下:1.基于壓縮感知的大規(guī)模無(wú)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(wú)線傳感器網(wǎng)絡(luò)的數(shù)據(jù)收集應(yīng)用中,研究了單匯聚節(jié)點(diǎn)和多匯聚節(jié)點(diǎn)的數(shù)據(jù)收集網(wǎng)絡(luò)的網(wǎng)絡(luò)容量及傳輸時(shí)延問(wèn)題。針對(duì)單匯聚節(jié)點(diǎn)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(shí)延性能。針對(duì)多匯聚節(jié)點(diǎn)的數(shù)據(jù)收集網(wǎng)絡(luò),首次引入了稀疏隨機(jī)投影理論,提出了基于壓縮感知的多會(huì)話數(shù)據(jù)收集方法;給出了多匯聚節(jié)點(diǎn)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(huì)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(shí)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(wú)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(shí)延。2.基于壓縮感知的的網(wǎng)間計(jì)算基于壓縮感知的數(shù)據(jù)傳輸在傳輸過(guò)程中通過(guò)將節(jié)點(diǎn)間的數(shù)據(jù)轉(zhuǎn)發(fā)轉(zhuǎn)化為節(jié)點(diǎn)間的數(shù)據(jù)計(jì)算,從而降低整個(gè)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(duì)降低無(wú)線傳感器網(wǎng)絡(luò)的傳輸能耗到底帶來(lái)多大的優(yōu)勢(shì)是一個(gè)值得研究的課題。本文將壓縮感知理論中對(duì)隨機(jī)投影的構(gòu)造轉(zhuǎn)化為對(duì)一個(gè)多輪隨機(jī)線性目標(biāo)函數(shù)的計(jì)算,提出了基于樹結(jié)構(gòu)以及基于流言的計(jì)算協(xié)議來(lái)實(shí)現(xiàn)基于壓縮感知的網(wǎng)間計(jì)算,首次從網(wǎng)間計(jì)算的角度評(píng)估壓縮感知在傳輸能耗及傳輸時(shí)延上的性能表現(xiàn)。針對(duì)基于樹結(jié)構(gòu)的計(jì)算協(xié)議,提出了在最優(yōu)的計(jì)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(shí)間相關(guān)性時(shí)用于進(jìn)一步提高計(jì)算性能的塊計(jì)算協(xié)議。針對(duì)基于流言的計(jì)算協(xié)議,提出了一種廣播流言算法,以實(shí)現(xiàn)網(wǎng)絡(luò)拓?fù)湟鬃兦闆r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(jì)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(shí)延。3.基于壓縮感知理論及隨機(jī)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(jī)游走的無(wú)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(jī)游走路徑步長(zhǎng)及所需的隨機(jī)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(jìn)行信號(hào)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(diǎn)需均勻采樣的限制,為壓縮感知理論在無(wú)線傳感器網(wǎng)絡(luò)中的應(yīng)用提供了一種更切實(shí)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(chǔ)空間小、計(jì)算復(fù)雜度低以及傳輸能耗低等優(yōu)點(diǎn)。
[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(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP212.9;TN929.5

【共引文獻(xiàn)】

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

3 查長(zhǎng)軍;分布式壓縮感知及輪廓識(shí)別研究[D];安徽大學(xué);2013年

4 丁昕苗;基于多示例學(xué)習(xí)的恐怖視頻識(shí)別技術(shù)研究[D];中國(guó)礦業(yè)大學(xué)（北京）;2013年

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

7 宋相法;基于稀疏表示和集成學(xué)習(xí)的若干分類問(wèn)題研究[D];西安電子科技大學(xué);2013年

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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(xué);2013年

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

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5 仇樂(lè)樂(lè);無(wú)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(xué);2013年

6 文首先;壓縮感知匹配追蹤算法的研究[D];安徽大學(xué);2013年

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本文編號(hào)：1840058