本文關(guān)鍵詞：車載網(wǎng)絡(luò)中基于移動軌跡預(yù)測的快速鄰居發(fā)現(xiàn)算法研究　出處：《天津大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： VANET 鄰居發(fā)現(xiàn) 移動預(yù)測 卡爾曼濾波

【摘要】：車載網(wǎng)絡(luò)中節(jié)點的快速移動導(dǎo)致網(wǎng)絡(luò)拓?fù)漕l繁變化,快速的鄰居發(fā)現(xiàn)算法成為影響網(wǎng)絡(luò)協(xié)議性能的重要因素。傳統(tǒng)移動網(wǎng)絡(luò)中,有很多可以用來提高數(shù)據(jù)傳輸性能和效率的協(xié)議,如路由協(xié)議、HELLO協(xié)議等。HELLO協(xié)議用于鄰居發(fā)現(xiàn)和鄰居維護(hù),常與路由協(xié)議相結(jié)合以提高路由協(xié)議的性能。在車載自組織網(wǎng)絡(luò)中,如果每個節(jié)點的鄰居表越精確,那么節(jié)點間的數(shù)據(jù)包投遞率就會越高。本文提出了一種新型的基于卡爾曼濾波器移動軌跡預(yù)測的HELLO協(xié)議,KFH(Kalman Filter-based HELLO protocol)。該協(xié)議將時間域劃分為相同長度的時隙,每一個節(jié)點擁有一個基于卡爾曼濾波器的預(yù)測模型,根據(jù)網(wǎng)絡(luò)環(huán)境中車輛移動的時間和空間特性來預(yù)測節(jié)點的運動軌跡。當(dāng)節(jié)點運用該模型預(yù)測自己下一個時隙的位置時,也同時對鄰居表中的每個鄰居進(jìn)行預(yù)測。如果節(jié)點的位置預(yù)測精度大于一定的閾值,將廣播一個hello探測信息,同時把自己的真實位置信息發(fā)送給鄰居車輛,接收到該探測信息的節(jié)點將使用最新的數(shù)據(jù)更新自己鄰居表中相應(yīng)的模型參數(shù)。在每個時隙中,節(jié)點使用自己對鄰居節(jié)點的位置預(yù)測數(shù)據(jù),計算自己與每個鄰居之間的距離,超出節(jié)點通信范圍的鄰居將會被刪除。因此,每個節(jié)點始終維護(hù)著一個最新最精確的鄰居表。通過仿真,將KFH與自回歸HELLO協(xié)議(Autoregressive Hello protocol,ARH),以及廣泛使用的基于一定時間間隔的HELLO協(xié)議進(jìn)行了對比。結(jié)果表明,KFH可以實現(xiàn)高效率的鄰居發(fā)現(xiàn),提高HELLO協(xié)議的性能。在同樣網(wǎng)絡(luò)開銷情況下,KFH具有最低的鄰居發(fā)現(xiàn)錯誤率(只有2%)及鄰居發(fā)現(xiàn)延遲。
[Abstract]:Fast moving nodes in vehicular networks leads to frequent change of network topology, fast neighbor discovery algorithm has become an important factor affecting the performance of network protocols. The traditional mobile network, there are many can be used to improve the performance and efficiency of data transmission protocols, such as routing protocol, HELLO protocol and.HELLO protocol for neighbor neighbor discovery and maintenance, often in combination in order to improve the performance of routing protocol and routing protocol. In thevanet, if each node's neighbor table more accurate, so inter node packet delivery ratio will be higher. This paper proposes a method based on Calman filter trajectory prediction HELLO protocol model, KFH (Kalman Filter-based HELLO protocol). The agreement will in time domain is divided into time slots with the same length, each node has a prediction model based on Calman filter, according to the network environment in China The characteristics of time and space vehicles to predict the trajectory of mobile nodes. When a node using the model to predict his position for the next slot, but also to each neighbor table are predicted. If the node position accuracy is greater than a certain threshold value will broadcast a hello detection information. At the same time their true position information is sent to the neighbor node receives the vehicle detection information will use the latest data to update the model parameters of their neighbor tables. In each time slot, the nodes use their neighbor nodes to forecast the location of data, the calculation between himself and the distance to each of its neighbors, the neighbors will be beyond the node communication range deleted. Therefore, each node always maintains a most accurate neighbor table. Through the simulation, KFH and autoregressive (Autoregressive Hello protocol HELLO, ARH), and Compared with widely used a time interval based on the HELLO protocol. The results show that KFH can achieve high efficiency of neighbor discovery, improve the performance of HELLO protocol. The network overhead in the same case, KFH has the lowest neighbor discovery error rate (only 2%) and neighbor discovery delay.

【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TN929.5

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本文編號：1407633