臺灣博碩士論文加值系統

隨著資訊化的普遍，各種嵌入式多媒體功能的出現，使得用戶所產生的內容不斷增加，為了解決未來IP不夠使用以及日益劇增的內容資訊，一種以內容命名作為連結的新型網路技術出現，稱之為內容中心網路(Content Centric Networking, CCN)。也因CCN 被視為未來網際網路發展最有潛力的一種新型架構，預計可優化車載行動通訊網路(Vehicular ad-hoc network, VANET) 中的各種應用。但是，車載內容中心網路(Vehicular Content Centric Networks, VCCN) 還面臨著一些挑戰，其中最急需解決的是內容緩存位置選擇，每個節點的緩存空間是有限的，必須制定新的緩存策略才能有效提升VCCN 整體性能。
本研究應用類神經網路(Neural Network, NN) 來模擬用戶行為並預測其對新內容的響應，我們提出載具移動紀錄(Vehicle Movement Record, VMR)方法，學習不同用戶習慣來預測接收到的資料是否需要緩存下來，不僅可以避免載具緩存不必要的資料，又能幫助資料很有彈性和靈活性的緩存在VCCN 整體環境中。

With the generalization of information, there are emergence of various embedded multimedia features, make the content increase constantly. In order to solve the problem of insufficient IP usage and increasing content information in the future. A new communication paradigm, namely Content Centric Networking (CCN), a new type of network technology that uses content naming as a link. CCN is regarded as the most promising new type of architecture for future Internet development and is expected to support various applications in vehicular communications (Vehicular ad-hoc network, VANET). There are still some challenges in Vehicular Content Centric Networks (VCCN). The most important is content cache location selection. Because each node's cache space is limited, it have to developed a new caching strategy to effectively improve the overall VCCN performance.
This study uses a neural network (NN) to simulate user behavior and predict its response to new content. We propose the Vehicle Movement Record (VMR) method to learn behaviors to predict whether the received data needs to be cached. This method not only can let the vehicle avoid caching unnecessary data, it can also help the data to be elastic and flexible in the VCCN environment.

摘要 i
Abstract ii
目錄 iii
圖目錄 iv
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 2
1.4論文架構 3
第二章 文獻探討 4
2.1內容中心網路 4
2.2車載內容中心網路 9
第三章 系統架構 11
3.1問題描述 11
3.2車載學習模式 11
3.3訓練框架 16
第四章 緩存策略與應用模擬 18
4.1緩存策略 18
4.2應用模擬 24
第五章 結論與未來發展 26
5.1結論 26
5.2未來研究方向 27
參考文獻 28

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