本文選題：無線傳感器網(wǎng)絡(luò)　切入點：三峽庫區(qū)　出處：《重慶大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：舉世矚目的長江三峽水利工程，已在我國社會經(jīng)濟(jì)發(fā)展中發(fā)揮重要作用。三峽水庫水環(huán)境安全問題一直倍受關(guān)注。而現(xiàn)有的監(jiān)測設(shè)備、方法和手段已不能滿足監(jiān)測任務(wù)需求，迫切需要提高三峽庫區(qū)水環(huán)境監(jiān)測的能力。無線傳感器網(wǎng)絡(luò)具有低成本、微型化、低能耗、自組織等特點，可用于三峽庫區(qū)水環(huán)境監(jiān)測，便于實現(xiàn)對庫區(qū)水環(huán)境狀況進(jìn)行連續(xù)、實時監(jiān)測和預(yù)警。監(jiān)測應(yīng)用中，節(jié)點提供的監(jiān)測數(shù)據(jù)必須帶有位置信息，才能準(zhǔn)確判斷可能存在的污染源。為此，本文在充分利用現(xiàn)有成果的基礎(chǔ)上，就應(yīng)用于三峽庫區(qū)水環(huán)境監(jiān)測的大規(guī)模無線傳感器網(wǎng)絡(luò)節(jié)點的定位問題展開研究。 根據(jù)三峽庫區(qū)獨特的地理分布特征，構(gòu)建的大規(guī)模無線傳感器網(wǎng)絡(luò)呈現(xiàn)出具有明顯帶狀分布特性，常出現(xiàn)C型和Y型等特殊區(qū)域分布網(wǎng)絡(luò)，這給節(jié)點定位帶來很大困難�，F(xiàn)有的定位算法很少考慮特殊復(fù)雜環(huán)境因素的影響，而且節(jié)點在完成定位后無法獲知其位置的準(zhǔn)確性，只能通過仿真實驗數(shù)據(jù)進(jìn)行評估。為此，本文在項目實際需求的基礎(chǔ)上，結(jié)合三峽庫區(qū)大規(guī)模無線傳感器網(wǎng)絡(luò)帶狀區(qū)域特性，將理論分析與仿真實驗結(jié)合，提出了適用于三峽庫區(qū)水環(huán)境監(jiān)測網(wǎng)絡(luò)節(jié)點的定位算法。論文主要工作如下： ①深入分析三峽庫區(qū)傳感器網(wǎng)絡(luò)節(jié)點分布特性，并分別對復(fù)雜區(qū)域下的各向同性和各向異性網(wǎng)絡(luò)進(jìn)行研究。將博弈論用于傳感器節(jié)點定位，建立了基于博弈論的傳感器節(jié)點定位模型，并對模型求解。 ②給出了基于博弈論的定位算法的具體實現(xiàn)步驟；采用MATLAB仿真平臺，驗證該算法在復(fù)雜分布區(qū)域下傳感器網(wǎng)絡(luò)中節(jié)點的定位效果，并與另外幾種算法相對比。實驗證明該算法在各向同性和各向異性網(wǎng)絡(luò)中均取得良好定位效果。 ③考慮在具體應(yīng)用環(huán)境中，由于網(wǎng)絡(luò)中采用某種算法進(jìn)行節(jié)點定位的誤差值難以獲得，利用統(tǒng)計學(xué)中的抽樣推斷理論，，提出一種基于錨節(jié)點的定位誤差估計方法，實現(xiàn)具體網(wǎng)絡(luò)中節(jié)點定位誤差估算。通過對多組網(wǎng)絡(luò)進(jìn)行實驗，分析估算效果，結(jié)果表明該方法能夠有效估計具體網(wǎng)絡(luò)中的節(jié)點定位誤差。
[Abstract]:The three Gorges Water Conservancy Project, which attracts worldwide attention, has played an important role in the social and economic development of our country. The problem of water environment safety of the three Gorges Reservoir has been paid close attention to. However, the existing monitoring equipment, methods and means can no longer meet the needs of the monitoring task. It is urgent to improve the ability of water environment monitoring in the three Gorges Reservoir area. Wireless sensor network has the characteristics of low cost, miniaturization, low energy consumption, self-organization, etc. It can be used for monitoring the water environment of the three Gorges Reservoir area, and it is convenient to carry out continuous water environment monitoring in the three Gorges Reservoir area. Real-time monitoring and early warning. In monitoring applications, monitoring data provided by nodes must have location information in order to accurately determine possible sources of pollution. The localization of large scale wireless sensor network nodes used in water environment monitoring in the three Gorges Reservoir area is studied. According to the unique geographical distribution characteristics of the three Gorges Reservoir area, the large scale wireless sensor networks have obvious zonal distribution characteristics, such as C type and Y type special regional distribution networks. This brings great difficulties to node location. Existing localization algorithms seldom consider the influence of special and complex environmental factors, and the node can not get the accuracy of its location after the location has been completed, so it can only be evaluated by simulation experimental data. On the basis of the actual demand of the project, this paper combines the theoretical analysis with the simulation experiment, combined with the zonal characteristics of the large-scale wireless sensor network in the three Gorges Reservoir area. This paper presents an algorithm for locating nodes in the water environment monitoring network of the three Gorges Reservoir area. The main work of this paper is as follows:. 1. The distribution characteristics of sensor network nodes in the three Gorges Reservoir area are analyzed, and the isotropic and anisotropic networks in complex regions are studied respectively. The game theory is used to locate sensor nodes. A sensor node location model based on game theory is established and solved. (2) the implementation steps of the localization algorithm based on game theory are given, and the localization effect of the algorithm in the sensor network is verified by using the MATLAB simulation platform. Compared with other algorithms, the experimental results show that the proposed algorithm achieves good localization performance in both isotropic and anisotropic networks. Considering that in the specific application environment, because it is difficult to obtain the error value of node location using some algorithm in the network, using the sampling inference theory in statistics, a localization error estimation method based on anchor node is proposed. The estimation of node location error in a specific network is realized. The results show that the proposed method can effectively estimate the node location error in a specific network by means of experiments on multiple networks and analysis of the estimation results.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV697.2;TN929.5;TP212.9

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