本文關(guān)鍵詞： 無(wú)線傳感器網(wǎng)絡(luò) 免疫機(jī)理 故障診斷 空間特性　出處：《重慶三峽學(xué)院》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：無(wú)線傳感網(wǎng)絡(luò)在信息采集、檢測(cè)等方面具有強(qiáng)大的處理功能,在復(fù)雜問題求解方面具有最優(yōu)求解能力,但其節(jié)點(diǎn)具有能量受限、路由狀態(tài)多變、通信易受干擾、發(fā)生故障概率較大等特點(diǎn),如何有效及時(shí)診斷出發(fā)生的故障已經(jīng)成為無(wú)線傳感器網(wǎng)絡(luò)應(yīng)用的關(guān)鍵問題。本文通過(guò)對(duì)空間特性下的節(jié)點(diǎn)故障診斷算法研究的基礎(chǔ)上,引入對(duì)故障檢測(cè)與診斷具有記憶識(shí)別、學(xué)習(xí)能力等優(yōu)點(diǎn)的免疫機(jī)理,通過(guò)對(duì)生物免疫系統(tǒng)的理論、仿生機(jī)理和人工免疫系統(tǒng)的理解,利用免疫機(jī)理的記憶學(xué)習(xí)的優(yōu)點(diǎn),對(duì)故障數(shù)據(jù)庫(kù)進(jìn)行實(shí)時(shí)優(yōu)化,本文圍繞著針對(duì)節(jié)點(diǎn)故障診斷開展工作,提出了基于空間特性下的節(jié)點(diǎn)免疫故障診斷算法(Node immune fault diagnosis algorithm,NIFD算法),為無(wú)線傳感網(wǎng)絡(luò)節(jié)點(diǎn)故障診斷提供了一種新方法。主要研究工作如下:1.通過(guò)對(duì)無(wú)線傳感網(wǎng)絡(luò)理論進(jìn)行系統(tǒng)性的梳理,對(duì)人工免疫系統(tǒng)的免疫機(jī)理分析,建立了人工免疫機(jī)理與無(wú)線傳感器網(wǎng)絡(luò)故障診斷之間的映射關(guān)系,利用人工免疫機(jī)理的記憶學(xué)習(xí)等優(yōu)點(diǎn)對(duì)節(jié)點(diǎn)數(shù)據(jù)故障庫(kù)進(jìn)行優(yōu)化。2.在節(jié)點(diǎn)診斷模型的基礎(chǔ)上,通過(guò)對(duì)節(jié)點(diǎn)的空間相關(guān)性的研究,在基于空間特性下的節(jié)點(diǎn)故障診斷算法基礎(chǔ)上,對(duì)網(wǎng)絡(luò)節(jié)點(diǎn)故障進(jìn)行可靠的檢測(cè),為了提高檢測(cè)的準(zhǔn)確度,引入免疫機(jī)理,建立了免疫故障診斷模型。3.通過(guò)對(duì)模型中免疫機(jī)制進(jìn)行分析,提出了 NIFD算法,實(shí)現(xiàn)對(duì)監(jiān)測(cè)區(qū)域內(nèi)故障節(jié)點(diǎn)的有效診斷。通過(guò)仿真實(shí)驗(yàn),分析該算法在故障節(jié)點(diǎn)的診斷精確度,虛警率和虛警概率等性能仿真,實(shí)現(xiàn)了對(duì)節(jié)點(diǎn)故障有效的檢測(cè)和診斷,判別出故障類型,提高了診斷的精度。建立的故障診斷模型能夠滿足節(jié)點(diǎn)故障的檢測(cè)與診斷要求,實(shí)現(xiàn)對(duì)故障的可靠性診斷。本文將基于空間特性的節(jié)點(diǎn)免疫診斷算法理論運(yùn)用到無(wú)線傳感網(wǎng)絡(luò)的節(jié)點(diǎn)檢測(cè)與診斷中,通過(guò)仿真驗(yàn)證該算法在節(jié)點(diǎn)故障檢測(cè)與診斷方面具有良好的性能,為解決無(wú)線傳感網(wǎng)絡(luò)的節(jié)點(diǎn)故障診斷問題提供參考。
[Abstract]:Wireless sensor networks have powerful processing functions in information collection and detection, and optimal solving ability in complex problem solving. However, the nodes of wireless sensor networks are energy limited, routing state is changeable, and communication is vulnerable to interference. How to diagnose the fault effectively and timely has become the key problem in wireless sensor network application. Based on the research of node fault diagnosis algorithm based on spatial characteristics, this paper discusses how to diagnose the fault effectively and in time. This paper introduces the immune mechanism which has the advantages of memory recognition and learning ability in fault detection and diagnosis. By understanding the theory of biological immune system, bionic mechanism and artificial immune system, the advantage of memory learning of immune mechanism is utilized. To optimize the fault database in real time, this paper focuses on node fault diagnosis. A node immune fault diagnosis algorithm named Node immune fault diagnosis algorithm based on spatial characteristics is proposed, which provides a new method for node fault diagnosis in wireless sensor networks. The main research work is as follows: 1. On systematic carding, Based on the analysis of the immune mechanism of the artificial immune system, the mapping relationship between the artificial immune mechanism and the fault diagnosis of the wireless sensor network is established. The memory learning of artificial immune mechanism is used to optimize the node data fault database. 2. Based on the node diagnosis model and the research of node spatial correlation, the node fault diagnosis algorithm based on spatial characteristics is proposed. In order to improve the accuracy of detection, the immune fault diagnosis model .3. is established in order to improve the accuracy of network node fault detection. Through the analysis of immune mechanism in the model, the NIFD algorithm is proposed. Through simulation experiments, this paper analyzes the performance simulation of the algorithm in fault node diagnosis accuracy, false alarm rate and false alarm probability, and realizes the effective detection and diagnosis of node fault. The fault diagnosis model can meet the requirements of node fault detection and diagnosis. In this paper, the theory of node immune diagnosis algorithm based on spatial characteristics is applied to node detection and diagnosis of wireless sensor networks. The simulation results show that the algorithm has good performance in node fault detection and diagnosis, and provides a reference for solving the node fault diagnosis problem in wireless sensor networks.
【學(xué)位授予單位】：重慶三峽學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.5;TP212.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 夏曉峰;何常勝;;LSM結(jié)合鄰居干擾抵抗模型的傳感器網(wǎng)絡(luò)節(jié)點(diǎn)故障檢測(cè)[J];湘潭大學(xué)自然科學(xué)學(xué)報(bào);2016年01期

2 胡順仁;李瑞平;包明;張建科;;基于主元分析的橋梁撓度傳感器故障診斷研究[J];傳感器與微系統(tǒng);2014年06期

3 張成;;基于聚類中值比較的WSNs故障檢測(cè)算法[J];傳感器與微系統(tǒng);2014年04期

4 董傳明;劉克中;羅廣;金湖庭;;無(wú)線傳感器網(wǎng)絡(luò)環(huán)境下的一種輕量級(jí)事件容錯(cuò)檢測(cè)算法[J];傳感技術(shù)學(xué)報(bào);2014年01期

5 蘆天亮;鄭康鋒;劉穎卿;胡影;武斌;;基于動(dòng)態(tài)克隆選擇算法的病毒檢測(cè)模型[J];北京郵電大學(xué)學(xué)報(bào);2013年03期

6 莊夏;戴敏;何元清;;基于人工免疫和模糊K均值的傳感器節(jié)點(diǎn)故障診斷[J];計(jì)算機(jī)測(cè)量與控制;2013年03期

7 劉韜;;基于梯度的無(wú)線傳感器網(wǎng)絡(luò)能耗分析及能量空洞避免機(jī)制[J];自動(dòng)化學(xué)報(bào);2012年08期

8 周春華;王運(yùn)成;陳冰;;傳感器網(wǎng)絡(luò)中時(shí)空關(guān)聯(lián)的臟數(shù)據(jù)過(guò)濾技術(shù)[J];計(jì)算機(jī)工程與設(shè)計(jì);2012年05期

9 陳擁軍;袁慎芳;吳鍵;張英杰;;基于免疫系統(tǒng)的無(wú)線傳感器網(wǎng)絡(luò)性能優(yōu)化[J];系統(tǒng)工程與電子技術(shù);2010年05期

10 季賽;袁慎芳;吳鍵;王水平;;基于時(shí)空特性的無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)故障診斷方法[J];傳感器與微系統(tǒng);2009年10期

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