本文關(guān)鍵詞： 壓縮感知 LDPC 稀疏隨機(jī)矩陣 能量均衡 無線傳感器網(wǎng)絡(luò)　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著大數(shù)據(jù)和人工智能時代的到來,物聯(lián)網(wǎng)再次成為了研究者關(guān)注的焦點(diǎn)。物聯(lián)網(wǎng)不僅給人們平時的生活帶來了很大的便利,同時在醫(yī)療健康、環(huán)境監(jiān)測、軍事偵查以及工業(yè)領(lǐng)域都有很大的應(yīng)用。無線傳感器網(wǎng)絡(luò),作為物聯(lián)網(wǎng)的核心技術(shù)之一,是由大量隨機(jī)分布的傳感器節(jié)點(diǎn)負(fù)責(zé)感知、收集、處理和分析數(shù)據(jù)信息,從而得到有效的信息并傳遞給用戶。但是由于節(jié)點(diǎn)的電量有限、存儲空間受限,因此,如何能夠融合這些大量的數(shù)據(jù),并且在傳輸?shù)倪^程中減少能量的消耗,延長網(wǎng)絡(luò)壽命變得尤為重要。此外,在一些實(shí)時性要求較高的場景下,如何將收集到的信息以最短的延遲盡快地傳遞到匯聚節(jié)點(diǎn)也是主要研究內(nèi)容之一。在無線傳感器網(wǎng)絡(luò)中,傳統(tǒng)的數(shù)據(jù)融合方式主要以減少數(shù)據(jù)冗余為目的,而不能夠大幅度減少數(shù)據(jù)包的傳輸量與傳感器的通信消耗,因此,本文利用所采集數(shù)據(jù)的時空相關(guān)性和傳感器網(wǎng)絡(luò)的自身特點(diǎn),通過稀疏設(shè)計(jì)測量矩陣,提出基于稀疏性壓縮感知的數(shù)據(jù)融合方法,從而減少網(wǎng)絡(luò)的傳輸數(shù)據(jù)包的數(shù)量量和能量消耗。本文的主要工作內(nèi)容如下:首先,針對傳統(tǒng)的壓縮感知的收集方式,本文提出了一種基于確定性二值的測量矩陣的測量算法,該算法構(gòu)造過程簡單快速。測量矩陣中的每一行代表一次測量過程,每次測量相互獨(dú)立。由于測量矩陣的稀疏性特點(diǎn),對應(yīng)矩陣中的非0元素的節(jié)點(diǎn)參與每次測量,參與同一個測量的節(jié)點(diǎn)的數(shù)據(jù)被融合成一個數(shù)據(jù)包傳遞到匯聚節(jié)點(diǎn)。當(dāng)匯聚節(jié)點(diǎn)收集到所有的測量值的時候,可以準(zhǔn)確地恢復(fù)原始數(shù)據(jù)。其次,針對傳感網(wǎng)中時延長和能量不均衡的問題,本文提出了一種基于稀疏隨機(jī)測量矩陣的融合算法。該算法在保證恢復(fù)原始數(shù)據(jù)的前提下,將測量過程分解為多個融合樹,單個融合樹是由部分節(jié)點(diǎn)參與。在傳遞數(shù)據(jù)的過程中,本文提出了一種減少時延的傳輸策略。另外,由于所設(shè)計(jì)矩陣的隨機(jī)性和稀疏性的特點(diǎn),節(jié)點(diǎn)的能量消耗能夠達(dá)到均衡,從而可以延長網(wǎng)絡(luò)壽命。最后,本文系統(tǒng)分析了所提出的算法,并對算法進(jìn)行實(shí)驗(yàn)驗(yàn)證。實(shí)驗(yàn)結(jié)果表明:對于能在頻域上稀疏表示的信號,采用確定性二值矩陣能夠有效地減少網(wǎng)絡(luò)能量消耗,而基于稀疏矩陣的低延遲且能量均衡的數(shù)據(jù)融合算法可以減少通信時延,均衡通信消耗。
[Abstract]:With the arrival of the era of big data and artificial intelligence, the Internet of things has become the focus of attention again. Internet of things not only brings great convenience to people's normal life, but also in the medical health, environmental monitoring. Wireless sensor network, as one of the core technologies of the Internet of things, is a large number of randomly distributed sensor nodes responsible for sensing and collection. Processing and analyzing the data information to get effective information and transfer to the user. However, because of the limited power of nodes, storage space is limited, so how to integrate these large amounts of data. And in the process of transmission to reduce energy consumption, extended network life has become particularly important. In addition, in some real-time requirements of the scene. How to transfer the collected information to the convergence node with the shortest delay is also one of the main research contents. In wireless sensor networks, the traditional data fusion mainly aims to reduce data redundancy. However, the communication consumption between the data packet and the sensor can not be greatly reduced. Therefore, based on the spatio-temporal correlation of the collected data and the characteristics of the sensor network, the sparse measurement matrix is designed. A data fusion method based on sparse compression sensing is proposed to reduce the amount of data packets and energy consumption. The main work of this paper is as follows: first. In view of the traditional collection method of compression perception, this paper presents a measurement algorithm based on deterministic binary measurement matrix. The algorithm is simple and fast. Each line in the measurement matrix represents a measurement process. Each measurement is independent of each other. Because of the sparsity of the measurement matrix, the nodes corresponding to the non-zero elements in the matrix participate in each measurement. The data of the node involved in the same measurement is fused into a packet and delivered to the sink node. When the sink node collects all the measured values, the original data can be recovered accurately. Secondly. In order to solve the problem of time prolongation and energy imbalance in sensor networks, a fusion algorithm based on sparse random measurement matrix is proposed, which can restore the original data. The measurement process is decomposed into multiple fusion trees, and a single fusion tree is joined by some nodes. In the process of data transfer, this paper proposes a transmission strategy to reduce delay. Because of the randomness and sparsity of the designed matrix, the energy consumption of the nodes can reach equilibrium, which can prolong the network life. Finally, the proposed algorithm is systematically analyzed in this paper. The experimental results show that the deterministic binary matrix can effectively reduce the network energy consumption for the signals which can be represented sparsely in frequency domain. The low delay and energy equalization data fusion algorithm based on sparse matrix can reduce the communication delay and equalize the communication consumption.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5;TP202

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本文編號：1466275