本文選題：車載網(wǎng)　切入點(diǎn)：群算法　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：近幾年,隨著云計(jì)算,大數(shù)據(jù)等新興技術(shù)的發(fā)展,智慧城市、智能交通等概念也走進(jìn)人們的視野。車載網(wǎng)以其獨(dú)特的魅力和廣闊的市場(chǎng)前景吸引人們的目光。因此,針對(duì)車載網(wǎng)路由算法的研究也成為了一個(gè)熱點(diǎn)。本文結(jié)合車載網(wǎng)自身的特點(diǎn),分析了Q-Learning,蟻群算法和模糊邏輯推理,以及目前存在的一些路由算法。針對(duì)一些算法的缺點(diǎn),得到改進(jìn)方法,并通過(guò)仿真實(shí)驗(yàn)證明,改進(jìn)后的算法的可行性。本文研究了Q-Learning和蟻群算法在車載網(wǎng)路由算法中的應(yīng)用。分析Q-ABR算法,指出其信息素更新方式的不足之處,在此基礎(chǔ)上提出信息素更新的改進(jìn)方法。針對(duì)算法設(shè)計(jì)和建模過(guò)程中沒(méi)有考慮路由回路產(chǎn)生,路由出錯(cuò)處理機(jī)制的問(wèn)題,重新設(shè)計(jì)算法流程,彌補(bǔ)算法設(shè)計(jì)的不足使其更適用于車載網(wǎng)的環(huán)境,并重新對(duì)該算法建模。隨后,文中分析了AODV算法,指出采用跳數(shù)作為鏈路度量尋找最短路徑的不足之處�？紤]車載網(wǎng)的特點(diǎn),利用模糊邏輯估計(jì)兩個(gè)節(jié)點(diǎn)之間的鏈路質(zhì)量,對(duì)Q-Learning進(jìn)行改進(jìn)。改變傳統(tǒng)Q-Learning中折扣率不能根據(jù)具體的實(shí)際情況發(fā)生自適應(yīng)變化的缺點(diǎn)。利用模糊邏輯估計(jì)鏈路質(zhì)量并用得到的值替代Q-Learning中的折扣率。用Q-Learning算法對(duì)AODV算法進(jìn)行改進(jìn),同時(shí)重新對(duì)該算法進(jìn)行建模。文中分析了車載網(wǎng)中負(fù)載均衡的問(wèn)題和AD-AODV負(fù)載均衡算法的不足之處。指出AD-AODV算法采用的鏈路度量方式的缺陷,針對(duì)該問(wèn)題提出新的鏈路代價(jià)函數(shù)。同時(shí)對(duì)AODV算法進(jìn)行改進(jìn),以實(shí)現(xiàn)負(fù)載均衡的策略并對(duì)改進(jìn)后的算法重新建模。仿真實(shí)驗(yàn)結(jié)果表明所設(shè)計(jì)的算法與其它算法相比較具有較好的性能。
[Abstract]:In recent years, with the development of cloud computing, big data and other emerging technologies, the concepts of intelligent city and intelligent transportation have also come into people's view. The vehicular network attracts people's attention with its unique charm and broad market prospect. According to the characteristics of the vehicle network, this paper analyzes Q-Learning, ant colony algorithm and fuzzy logic reasoning, as well as some existing routing algorithms. This paper studies the application of Q-Learning and Ant Colony algorithm in vehicle network routing algorithm, analyzes Q-ABR algorithm, and points out the deficiency of pheromone updating method. Based on this, an improved method of pheromone updating is put forward. In the process of algorithm design and modeling, the routing loop generation and routing error handling mechanism are not considered in the process of algorithm design and modeling, and the algorithm flow is redesigned. The algorithm is more suitable for the vehicle network environment, and the algorithm is modeled again. Then, the AODV algorithm is analyzed, and the deficiency of using hop number as the link metric to find the shortest path is pointed out, and the characteristics of the vehicle network are considered. Using fuzzy logic to estimate the link quality between two nodes, Q-Learning is improved. The disadvantage of changing the discount rate in traditional Q-Learning can not be adapted to the actual situation. The link quality is estimated by fuzzy logic and the obtained value is used to replace the discount rate in Q-Learning. The AODV algorithm is improved, The problem of load balancing in vehicle network and the deficiency of AD-AODV load balancing algorithm are analyzed in this paper. The defect of link measurement method used in AD-AODV algorithm is pointed out. To solve this problem, a new link cost function is proposed, and the AODV algorithm is improved. The simulation results show that the proposed algorithm has better performance compared with other algorithms.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TN929.5

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本文編號(hào)：1623851