本文選題：移動自組織網(wǎng)絡(luò) + 鏈路連通性�。� 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：移動自組織網(wǎng)絡(luò)(Mobile Ad-hoc Networks,MANET)是一種無固定接入點、節(jié)點自我組織和管理的無線通信系統(tǒng),組網(wǎng)靈活,應(yīng)用前景廣闊。在移動自組織網(wǎng)絡(luò)中,節(jié)點間的通信路徑由一系列無線鏈路組成,鏈路性能直接影響網(wǎng)絡(luò)通信質(zhì)量,而鏈路最基本的特性是鏈路連通性。通信節(jié)點的移動性使得鏈路連通情況頻頻變化,導(dǎo)致網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)和通信路由等也隨之變化。因此,針對動態(tài)網(wǎng)絡(luò)的鏈路連通性研究對網(wǎng)絡(luò)拓?fù)淇刂坪吐酚蓞f(xié)議分析等有著重要的意義。鏈路連通性主要受無線鏈路傳輸環(huán)境和節(jié)點移動特性影響。根據(jù)無線電磁波在空中傳播的損耗特性,理論推導(dǎo)了信號功率的三種衰落模型:路徑損耗、陰影衰落和多徑衰落。利用信號功率衰落特性,引入節(jié)點有效傳輸范圍模型。通過仿真實驗比較三種衰落模型不同組合下節(jié)點有效傳輸環(huán)境的變化情況,分析無線鏈路傳輸環(huán)境對鏈路連通性的影響。同時,對比研究三種常見運動模型:隨機行走模型、高斯-馬爾可夫移動模型和半馬爾可夫平滑移動模型。通過實驗仿真單個節(jié)點運動軌跡和某時刻所有節(jié)點的空間分布,評估三種模型的優(yōu)缺點。然后,選取運動規(guī)律更貼近實際情況的半馬爾可夫移動模型作為后續(xù)研究的節(jié)點運動模型。在此基礎(chǔ)上,利用圖論的鄰接矩陣和鏈路連通概率向量表示鏈路連通性,結(jié)合馬爾可夫鏈理論,建立具有時變特性的一階馬爾可夫鏈路連通性模型。為了提高模型精度,依據(jù)高階馬爾可夫過程可以逼近任意可測過程的理論基礎(chǔ),將一階馬爾可夫鏈路連通性模型擴展到高階馬爾可夫鏈路連通性模型,最終建立具有時變特性和高精度的高階馬爾可夫鏈路連通性模型。通過蒙特卡洛仿真得到不同時刻鏈路連通狀態(tài),采用統(tǒng)計方法獲得鏈路連通性模型參數(shù):馬爾可夫轉(zhuǎn)移概率矩陣。為了驗證鏈路連通性模型的準(zhǔn)確性,通過與蒙特卡洛仿真、多狀態(tài)一階馬爾可夫模型實驗對比,評估網(wǎng)絡(luò)特性參數(shù):鏈路生命時間。并分析模型精度與馬爾可夫鏈階數(shù)之間的對應(yīng)關(guān)系,優(yōu)化模型。通過仿真實驗得到以下結(jié)論:首先,高階馬爾可夫鏈路連通性模型能夠有效地描述無線鏈路隨時間變化的連通情況;其次,高階馬爾可夫鏈路連通性模型生成的鏈路生命時間精度隨著馬爾可夫鏈階數(shù)增加而提升,當(dāng)馬爾可夫鏈階數(shù)大于四時,模型精度提升不明顯;最后,相比多狀態(tài)一階馬爾可夫模型,四階馬爾可夫鏈路連通性模型的模型誤差下降了68%。
[Abstract]:Mobile Ad-hoc Networks (Manet) is a wireless communication system with no fixed access point and node self-organization and management. In mobile ad hoc networks, the communication path between nodes is composed of a series of wireless links. The link performance directly affects the communication quality of the network, and the most basic characteristic of the link is link connectivity. The mobility of communication nodes causes frequent changes in link connectivity, resulting in changes in network topology and communication routing. Therefore, the research on link connectivity of dynamic networks is of great significance to network topology control and routing protocol analysis. Link connectivity is mainly affected by wireless link transmission environment and node mobility. According to the loss characteristics of wireless electromagnetic wave propagation in the air, three fading models of signal power are derived theoretically: path loss, shadow fading and multipath fading. Based on the signal power fading characteristic, the effective transmission range model is introduced. The effect of wireless link transmission environment on link connectivity is analyzed by comparing the change of node effective transmission environment under different combinations of three fading models. At the same time, three common motion models are compared: random walking model, Gauss-Markov moving model and semi-Markov smooth moving model. The advantages and disadvantages of the three models are evaluated by simulating the motion trajectory of a single node and the spatial distribution of all nodes at a certain time. Then, the semi-Markov moving model, which is closer to the actual situation, is selected as the node motion model in the following research. On this basis, using the adjacency matrix of graph theory and link connected probability vector to express link connectivity, combining with Markov chain theory, a first-order Markov link connectivity model with time-varying characteristics is established. In order to improve the accuracy of the model, the first order Markov link connectivity model is extended to the high order Markov link connectivity model according to the theoretical basis that high order Markov processes can approach any measurable process. Finally, a high order Markov link connectivity model with time varying characteristics and high accuracy is established. The link connected states at different times are obtained by Monte Carlo simulation, and the link connectivity model parameters: Markov transition probability matrix are obtained by statistical method. In order to verify the accuracy of the link connectivity model, the link life time is evaluated by comparing it with Monte Carlo simulation and multi-state first-order Markov model. The relationship between the precision of the model and the order of Markov chain is analyzed to optimize the model. The simulation results are as follows: firstly, the high order Markov link connectivity model can effectively describe the connectivity of wireless links over time; secondly, The link lifetime accuracy generated by the higher-order Markov link connectivity model increases with the increase of Markov chain order. When the Markov chain order is greater than 04:00, the model accuracy is not improved obviously. Compared with the multi-state first-order Markov model, the model error of the fourth-order Markov link connectivity model is reduced by 68 degrees.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.5

【共引文獻】

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1 孫翔;李洪烈;李穎;周洪霞;;Ad Hoc網(wǎng)絡(luò)移動模型研究[J];四川兵工學(xué)報;2013年10期

2 劉杰;周柯;王瑩凱;;基于移動信標(biāo)的改進DV-Hop算法[J];傳感器世界;2013年09期

3 趙庶源;趙季紅;曲樺;;泛在網(wǎng)絡(luò)下的群組移動模型研究現(xiàn)狀及展望[J];電信科學(xué);2013年10期

4 黃曉敏;汪洋;張繼良;張乃通;;車聯(lián)網(wǎng)無線鏈路傳輸數(shù)據(jù)業(yè)務(wù)性能評估[J];北京郵電大學(xué)學(xué)報;2014年S1期

5 李曉凱;呂宏偉;;一種路點受限的移動傳感器網(wǎng)絡(luò)移動模型[J];傳感器與微系統(tǒng);2014年08期

6 姚宏;白長敏;梁慶中;胡成玉;樊媛媛;顏雪松;;基于公交車緩存機制的移動數(shù)據(jù)分流研究[J];廣西大學(xué)學(xué)報(自然科學(xué)版);2014年06期

7 陳德鴻;何欣;劉天須;;機會網(wǎng)絡(luò)移動模型綜述[J];信息安全與技術(shù);2015年05期

8 謝立春;;基于小波和ARIMA模型的業(yè)務(wù)流預(yù)測方法[J];計算機工程;2013年02期

9 蔡菁;王騰飛;;基于移動模型的車載自組織網(wǎng)絡(luò)仿真技術(shù)研究[J];華中科技大學(xué)學(xué)報(自然科學(xué)版);2013年S2期

10 王曉喃;錢煥延;;6LoWPAN無線傳感器網(wǎng)絡(luò)移動管理方案[J];計算機應(yīng)用研究;2014年01期

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1 黃曉敏;汪洋;張繼良;張乃通;;車聯(lián)網(wǎng)無線鏈路傳輸數(shù)據(jù)業(yè)務(wù)性能評估[A];2013年全國通信軟件學(xué)術(shù)會議論文集[C];2013年

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1 郭麗芳;MANET的拓?fù)鋭討B(tài)性研究[D];北京郵電大學(xué);2011年

2 王英赫;移動自組織網(wǎng)絡(luò)的拓?fù)溲葸M及路由策略研究[D];北京郵電大學(xué);2013年

3 張竹;IP/LEO衛(wèi)星網(wǎng)絡(luò)中的移動性管理技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2013年

4 李文霽;延遲容忍移動傳感器網(wǎng)絡(luò)數(shù)據(jù)傳輸技術(shù)研究[D];北京郵電大學(xué);2013年

5 倪e，

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