臺灣博碩士論文加值系統

Mobile ad hoc network(MANET)，是人類配戴短距離的收發裝置，藉由移動行為來傳遞訊息給每個人，每個裝置接扮演著中介者的角色，幫助所有人傳遞訊息，這種方法可以利用在無線建設不發達的地方，利用簡便的裝置以達到互相溝通的目的。
現有的研究包含傳遞的方式以及產生人類移動的紀錄檔，我們這篇文章著重於人類移動的紀錄，現有的研究包含隨機模型:Random walk、著重位置:SLAW、SWIM、著重人群:HCMM，等多種不同的類型。
我們採用能表現人群性質的HCMM加以改善強化，添加了停留時間、生活作息等等，將HCMM變得更貼近真實的紀錄，並提出一套流程有效的設置參數逐步的接近真實記錄的特性，讓模擬器更為方便的使用。
最後的部分展現出比較各個真實紀錄的結果，顯示改善的HCMM與真實記錄的特性是極為相似的，而在這之中又屬小範圍的表現較好，能夠表現出人群見面的諸多特性。

Mobile ad hoc network (MANET) is a human wearing a short distance receiving device to deliver a message to each person by moving behavior. Each device plays a router that helping everyone to pass message, this network type can be used in lacking of wireless infrastructure area. Use cheap and simple devices to deliver messages.

Recently researches included MANET forwarding strategies and generate human contact trace files. In this paper we focus on human contact trace. Existing research includes the random model: Random walk, location model: SLAW, SWIM, human cluster model: HCMM, and other different types of human mobile trace.

We think HCMM can display human clustering characteristic. We chose HCMM to be our simulator and enhance it to be better. Add pause time and human day life activity in order to close real trace and propose a process effectively to set the parameters. Make HCMM easy to use.

The final part of the paper shows that compared the results of various real trace and simulation trace. Display the characteristics of real trace and enhancement HCMM is very similar. The performance of small area is better than large area and clustering feature is better than random contact.

誌謝 I
摘要 II
Abstract III
目錄 V
圖表目錄 VI
1 Introduction 1
2 Related work: 3
2.1 隨機模型: 3
2.2空間為主模型: 4
2.3社群為主模型: 5
3. Introduce HCMM and characteristic 6
3.1 HCMM 6
3.2 Dataset 9
3.3 mobility traces attribute 10
4. Mechanism improvement 12
4.1 HCMM argument 12
4.2 adding new mechanism to HCMM 13
4.3 Step1-social relation and groups 13
4.4 Step2-Activity node 15
4.5 Step3-pause time 17
4.6 Step4-cell size 19
4.7 Step5-contact assumption 20
4.8 Step5-Random mechanism 21
5. Performance 24
6. Conclusion and Future Work 36
Reference 38

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