發(fā)布時間：2018-05-21 08:31

  本文選題：無線傳感網(wǎng)絡(luò) + 蟲洞　； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(WSN)為人類提供了一種可以從數(shù)據(jù)角度去感知客觀世界的方式,其研究前景廣闊。隨著近年來的迅速發(fā)展,已在軍事、工業(yè)、環(huán)境監(jiān)測、醫(yī)療健康和日常生活中扮演著越來越重要的角色。無線傳感器網(wǎng)絡(luò)作為物聯(lián)網(wǎng)中的一種核心技術(shù),它在物聯(lián)網(wǎng)中與其他技術(shù)協(xié)同工作,共同所產(chǎn)生的經(jīng)濟效率和社會效益遠遠大于單種技術(shù)產(chǎn)生效益的總和。無線傳感器網(wǎng)絡(luò)與有線網(wǎng)絡(luò)和傳統(tǒng)的無線網(wǎng)絡(luò)有著巨大的不同之處,它僅由許多傳感感器節(jié)點,沒有復(fù)雜、高效的管理設(shè)備用于管理網(wǎng)絡(luò)拓撲結(jié)構(gòu)與各種協(xié)議。節(jié)點間的數(shù)據(jù)通信依靠自身與其他節(jié)點的相互覆蓋而形成的無線鏈路。因為沒有集中式的管理設(shè)備,節(jié)點網(wǎng)絡(luò)拓撲的形成、管理和維護都只能依靠節(jié)點本身。而無線傳感器網(wǎng)絡(luò)的工作環(huán)境往往比較惡劣,其拓撲結(jié)構(gòu)中很有可能會出現(xiàn)蟲洞,從而帶來惡劣的影響,所以蟲洞檢測是無線傳感器網(wǎng)絡(luò)的中的一個重要研究課題。檢測和定位無線傳感器網(wǎng)絡(luò)的外邊界與蟲洞產(chǎn)生的內(nèi)邊界對網(wǎng)絡(luò)的穩(wěn)定性、工作效率與生命周期有著很強的關(guān)聯(lián)性。本文基于復(fù)雜網(wǎng)絡(luò)中的滲流理論對無線傳感器網(wǎng)絡(luò)的蟲洞進行研究。本研究在無法獲取節(jié)點位置信息的前提下,以檢測網(wǎng)絡(luò)中蟲洞的邊界與網(wǎng)絡(luò)的外邊界為目標,研究相關(guān)的檢測算法與參數(shù)優(yōu)化。本文主要內(nèi)容如下:1)利用滲流理論中網(wǎng)絡(luò)中某一點的狀態(tài)突變會引起系統(tǒng)全局狀態(tài)發(fā)生改變的特點,建立了回流模型�；亓髂Ｐ屯ㄟ^修改某個節(jié)點狀態(tài),然后分析處于不同位置節(jié)點的狀態(tài)改變情況;2)在回流模型的基礎(chǔ)上進行無線傳感器網(wǎng)絡(luò)的邊界節(jié)點的識別形成邊界森林,最后通過最短路徑算法將邊界森林形成閉環(huán);3)通過仿真軟件進行多組模擬實驗,對算法的準確性進行驗證。并分析部分參數(shù)的優(yōu)化。本文提出的算法可以檢測出90%以上的邊界節(jié)點,針對無線傳感感器中蟲洞檢測效果良好。
[Abstract]:Wireless sensor networks (WSNs) provide a way for human beings to perceive the objective world from the point of view of data. With the rapid development in recent years, it has played a more and more important role in military, industry, environmental monitoring, medical health and daily life. Wireless sensor network (WSN) is a core technology in the Internet of things. It works in collaboration with other technologies in the Internet of things, which results in far greater economic efficiency and social benefit than the sum of benefits produced by a single technology. Wireless sensor network has great difference from wired network and traditional wireless network. It is only composed of many sensor nodes without complex and efficient management equipment to manage network topology and various protocols. Wireless link formed by data communication between nodes by mutual coverage with other nodes. Because there is no centralized management equipment, node network topology formation, management and maintenance can only rely on the node itself. However, the working environment of wireless sensor networks is often bad, and wormholes are likely to appear in the topology of wireless sensor networks. Therefore, wormhole detection is an important research topic in wireless sensor networks. Detecting and locating the outer boundary of wireless sensor network and the inner boundary produced by wormhole have strong correlation with the network stability, work efficiency and life cycle. In this paper, wormholes of wireless sensor networks are studied based on percolation theory in complex networks. On the premise that the node location information can not be obtained, the detection of wormhole boundary and the outer boundary of the network are the targets of this study, and the related detection algorithms and parameter optimization are studied. The main contents of this paper are as follows: (1) based on the characteristic that the sudden change of the state at a certain point in the percolation theory can cause the change of the global state of the system, a reflux model is established. By modifying a node state and analyzing the state change of nodes in different locations, the reflux model is used to identify the boundary nodes of WSN to form a boundary forest based on the reflux model. Finally, the shortest path algorithm is used to form the boundary forest in closed loop. The simulation software is used to carry out multi-group simulation experiments to verify the accuracy of the algorithm. The optimization of some parameters is analyzed. The algorithm proposed in this paper can detect more than 90% of boundary nodes, and the detection effect of wormhole in wireless sensor is good.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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本文編號：1918480