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研究生: 許耿愷
Geng-Kai Syu
論文名稱: HCMM社交網路移動紀錄產生器的改善
Enhancement to HCMM for Mobile Social Network Trace Generation
指導教授: 陳秋華
Chyou-Hwa Chen
口試委員: 鄭欣明
Shin-Ming Cheng
金台齡
Tai-Ling Chin
邱舉明
Ge-Ming Chiu
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 48
中文關鍵詞: 人類移動模型無線隨意網路模擬器
外文關鍵詞: forwarding protocols in mobile networks, simulations, Mobility model
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  • 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 AbstractIII 目錄V 圖表目錄VI 1 Introduction1 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 characteristic6 3.1 HCMM6 3.2 Dataset9 3.3 mobility traces attribute10 4. Mechanism improvement12 4.1 HCMM argument12 4.2 adding new mechanism to HCMM13 4.3 Step1-social relation and groups13 4.4 Step2-Activity node15 4.5 Step3-pause time17 4.6 Step4-cell size19 4.7 Step5-contact assumption20 4.8 Step5-Random mechanism21 5. Performance24 6. Conclusion and Future Work36 Reference38

    1.P O S Vaz de Melo, A C Viana, M Fiore, K Jaffrès-Runser, Le F Mouël, A A F Loureiro. ”RECAST: Telling Apart Social and Random Relationships in Dynamic Networks,”
    2.Thomas Karagiannis, Jean-Yves Le Boudec and Milan Vojnovi´c : ”Power Law and Exponential Decay of Inter Contact Times between Mobile Devices”
    3.Andrea Ribeiro, Rute Sofia and André Zúquete :”Modeling Pause Time in Social Mobility Models”
    4.S. Kosta, A. Mei, and J. Stefa.:"Large-Scale Social Mobile Synthetic Networks with SWIM"
    5.Chiara Boldrini , Andrea Passarella:“HCMM: Modelling spatial and temporal properties of human mobility driven by users’ social relationships”
    6.Poria Pirozmand, Guowei Wu, Behrouz Jedari, Feng Xia :”Human mobility in opportunistic networks: Characteristics, models and prediction methods “
    7.Ari Kerぴanen, Jぴorg Ott, and Teemu Kぴarkkぴainen.:”The ONE Simulator for DTN Protocol Evaluation. In SIMUTools”
    8.Square Line Picking: http://mathworld.wolfram.com/SquareLinePicking.html
    9.Lecture Notes: Social Networks: Models, Algorithms, and Applications Lecture 3: Jan 24, 2012 Scribes: Geoffrey Fairchild and Jason Fries
    10.Dong-Hee Kim and Hawoong Jeong:”Scale-Free Spanning Trees of Complex Networks”
    11.Real contact trace: http://crawdad.org/index.html
    12.D. Endres and J. Schindelin:”A New Metric for Probability Distributions”

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    全文公開日期 2020/08/17 (國家圖書館:臺灣博碩士論文系統)
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