臺灣博碩士論文加值系統

在耐延遲網路(Delay Tolerant Networks, DTNs)中，來源端到目的端之間的 end-to-end 的永久路由路徑是不存在的。DTN 技術意旨在解決像是長距離傳輸、不確定性的網路拓撲、稀疏的節點密度和高移動性等環境因素所造成數據傳輸上的障礙。
不同於 DTN 中的傳統路由協議，地理路由策略是唯一不需要節點的網路地址來獲取網路中路由路徑的資訊。節點使用衛星定位系統或其他定位系統來決定路由模式和基於 store-carry-and-forward 方式來傳輸訊息的下一個中繼節點。然而在高動態網路環境中，地理路由模型難以根據節點移動的歷史記錄來預測節點的後續運動。這種情況影響了選擇下一個中繼節點的策略並減少傳遞訊息到目的地的機會。另外，
節點有限的儲存空間會更可能發生擁塞的問題。
本論文提出了一種適用於高動態 DTN 環境中的地理路由方案。
1. 該方案使用相遇節點的移動方向和來源端和目的端之間所建立的基線來獲得相遇節點的移動角度。

2. 在得到移動角度之後，該方案將按照特定條件來選擇下一個中繼節點。

3. 最後，根據相遇節點和基線之間的歐幾里得距離的大小，這個方案會排出訊息傳輸的順序。
因此，modified 的地理路由方案將根據路由策略給予不同的複製 payoffs 來增加成功傳送的可能性。此外，本論文設計了一個最佳化管理機制來最大化平均訊息傳遞率。我們聚焦在一個關鍵問題上：當 buffer size 是滿的時候，訊息應該首先被丟棄。我們開發一個使用 Local 的網路資訊的 utility function 進行最佳化每份訊息的
平均抵達率。訊息將根據 utility value 來被丟棄或保存。
最後，我們使用 ONE 模擬器與其他方案相比來檢視我們方案的成果。結果表示我們提出的方案在不同的模擬參數設定下，抵達率大概有 70％以上和較高的平均延遲時間。

In delay tolerant networks (DTNs), persistent end-to-end route paths from the source to the destination are not truely existed. The DTN technology aims to solve the challenges and obstacles of data transmission caused by environmental factors, such as long distance transmission, uncertain network topology, sparse node density and high mobility.
Unlike conventional routing protocols in DTNs, the geographic routing strategy is the only one that does not need the network addresses of nodes to obtain the information of the route paths in a network. Nodes use the satellite positioning system or other positioning
systems to decide a routing mode and the next relay node, which is chosen to transfer messages based on the store-carry-and-froward scheme. However the geographic routing
model is difficult to predict the follow-up movement of nodes based on historical records of node movement in high dynamic network environments. This situation affects the policy of choosing the next relay node and decreases the opportunity to deliver messages to the destination. In addition, the constrained buffer space of nodes makes the congestion problem more possible.
This thesis proposes a modified geographic routing scheme which is suitable in high dynamic DTN environment.
1. This scheme uses the encounter node’s moving direction and base line which is
established between source and destination to get the encounter node’s moving
angle.
2. After getting the moving angle, this scheme will follow specific conditions to choose the next relay node.
3. At last, according to the size of the Euclidean distance between an encounter node and base line, this scheme will row the queue of the message transmission.
Accordingly, the modified geographic routing scheme will give different copy payoffs to increase the probability of successful delivery according to the routing strategy. Furthermore, this thesis designs an optimal buffer management to maximize the average message delivery rate. We focus on one key issue: messages should be dropped first when the buffer is full. We develop a utility function using local network information to optimize
per-message of average delivery rate. Messages will be dropped or kept according to their utility values. Finally, we use the ONE simulator to examine the performance of our scheme in comparison with other schemes. The results indicate that the delivery rate of
our proposed scheme approximately has 70% and higher average delay in different value sets of parameters in simulation.

1 Introduction P.1
2 Research Background P.4
2.1 Geographic Routing P.4
2.1.1 Greedy Forwarding P.5
2.1.2 Problem of Greedy Forwarding P.6
2.2 Global Knowledge-Based Scheduling And Drop P.6
2.2.1 Maximizing the Average Delivery Rate P.7
3 Related Work P.9
3.1 Research on Geographic Routing P.9
3.2 Research on GBSD P.11
4 Geographic Routing with Optimal Buffer Management P.13
4.1 Motivation and Design Abstract P.13
4.2 System Model P.14
4.3 Method Design P.16
4.4 Relay Node Selection Phase P.17
4.5 Message replication Phase P.20
4.6 Optimal Buffer Management Phase P.20
5 Simulations P.31
5.1 Performance Metrics P.31
5.2 Mobility Model P.32
5.3 Comparison Method P.32
5.4 Environment Setting P.33
5.4.1 Environment Setting in Random Way Point P.33
5.4.2 Environment Setting in SLAW P.34
6 Results and Analysis – Random Way Point P.37
6.1 Influence of θ + P.37
6.2 Performance Analysis P.39
6.2.1 Results with Different values of TTL P.39
6.2.2 Results with Different values of Buffer Size P.42
6.2.3 Results with Different values of Quotas P.44
7 Results and Analysis – SLAW P.46
7.1 Influence of θ + P.46
7.1.1 Results with Different values of TTL P.48
8 Conclusions and Future Works P.51
Bibliography P.53

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