政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/122396

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/122396

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113318/144297 (79%)
造访人次 : 51081064 在线人数 : 970

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/122396

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/122396

题名:	行動群眾感知與計算的訊息蒐集與散播機制 Message Collection and Dissemination Approach Using Mobile Crowd Sensing and Computing
作者:	鄧皓 Teng, Hao
贡献者:	蔡子傑 Tsai, Tzu-Chieh 鄧皓 Teng, Hao
关键词:	耐延遲網路行動群眾感知與計算機會性行動網路個人興趣追蹤數據 Delay Tolerant Network Mobile crowd sensing and computing Opportunistic mobile networks Personal interests Trace data
日期:	2019
上传时间:	2019-03-04 21:34:26 (UTC+8)
摘要:	隨著行動裝置的普及，行動群眾感知與計算MCSC (mobile crowd sensing and computing)是一門新興的技術。強調利用群眾節點獨立的計算能力彼此交換訊息。我們希望能將MCSC的技術利用到DTN網路情境中，並結合霧運算的概念將資料做更進一步的處理。而其中最重要和最大的困難點就是如何設計routing方法並找出最有效的傳輸路徑。我們設計一個以機會性行動網路為主體DTN平台，並結合以校園學生歷史紀錄的NCCU trace data，設計以相遇機率為基礎的演算法，上傳節點攜帶的資料到邊緣，再利用餘弦相似性將有興趣組成的特定訊息，傳給對訊息有足夠興趣的使用者。最後我們會針對演算法進行模擬，並和其他傳統DTN路由協議演算法做比較，評估效能的提升與節點浪費的下降。 Mobile crowd sensing and computing (MCSC) is an emerging technology along with the popularity of mobile devices. We utilize the concept of Delay Tolerant Networks (DTNs) and fog computing to support the message collection and dissemination for the MCSC. We emphasize the use of the independent computing power of the mobile crowd nodes to exchange messages with each other. The most challenge here is to design an efficient routing method to deliver messages for both “upload” (data collection to edge nodes) and “download” (data dissemination to nodes that interest) paths. We assume that the mobile crowd nodes will like to exchange data in a delay tolerant network (DTN) manner based on opportunistic transmission in order to save energy and data transmission cost. We design a probability-based algorithm to upload data which carried by normal mobile nodes to the edge nodes. And then, we use cosine similarity to relay specific message of attributes to users who have high interest to receive the message. We simulate the algorithm with the NCCU real trace data of campus students, and compare it with other traditional DTN routing algorithms. The performance evaluations show the improvement of message delivery ratio and decreasing latency and transmission overhead.
參考文獻:	[1] Kempe, J. Kleinberg, and E ́. Tardos, Maximizing the spread of influence through a social network, ACM SIGKDD, 2003, pp. 137– 146. [2] W. Chen, Y. Wang, and S. Yang, Efficient influence maximization in social networks, ACM SIGKDD, 2009, pp. 199–208. [3] M. Han, M. Yan, Z. Cai, and Y. Li, An exploration of broader influence maximization in timeliness networks with opportunistic selection. Journal of Network and Computer Applications, 2016. [4] T.Shi, J.Wan, S.Cheng, Z.Cai, Y.Li, and J.Li,“ Time-bounded positive influence in social networks,” in IIKI, 2015. [5] GUO, Bin, et al. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys (CSUR), 2015, 48.1: 7 [6] Huadong Ma, Dong Zhao, Peiyan Yuan, et al. Opportunities in mobile crowd sensing. IEEE Communications Magazine, August, 2014. [7] HIGUCHI, Tatsuro, et al. A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks. Communications (ICC), 2014 IEEE International Conference on. IEEE, 2014. p.42-47. [8] MUN, Min, et al. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 2009. p.55-68. [9] XIANG, Chaocan, et al. Passfit: Participatory sensing and filtering for identifying truthful urban pollution sources. Sensors Journal, IEEE, 2013. [10] GAONKAR, Shravan, et al. Micro-blog: sharing and querying content through mobile phones and social participation. In: Proceedings of the 6th international conference on Mobile systems, applications, and services. ACM, 2008. p. 174-186. [11] N. Eagle and A. Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, Vol 10(4):255–268, May 2006. [12] P. Hui, People are the network: experimental design and evaluation of social-based forwarding algorithms, Ph.D. dissertation, UCAM-CL-TR-713. University of Cambridge, Comp.Lab., 2008 [13] Marin, Radu-Corneliu, Ciprian Dobre, and Fatos Xhafa. Exploring Predictability in Mobile Interaction. EIDWT. 2012. [14] HIGUCHI, Tatsuro, et al. A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks. Communications (ICC), 2014 IEEE International Conference on. IEEE, 2014. p. 42-47. [15] CAI, Ji Li Zhipeng; YAN, Mingyuan; LI, Yingshu. Using crowdsourced data in location-based social networks to explore influence maximization. The 35th Annual IEEE International Conference on Computer Communications (INFOCOM 2016). 2016. [16] QIN, Jun, et al. Post: Exploiting dynamic sociality for mobile advertising in vehicular networks. IEEE Transactions on Parallel and Distributed Systems, 2016. [17] Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran, et al. 2017. The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity. ACM New York, 2017. [18] Koustabh Dolui, Soumya Kanti Datta, et al. Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. IEEE Global Internet of Things Summit (GIoTS), 2017. [19] TSAI, Tzu-Chieh; CHAN, Ho-Hsiang. NCCU Trace: social-network-aware mobility trace. Communications Magazine, IEEE, 2015. [20] TSAI, Tzu-Chieh, et al. A Social Behavior Based Interest-Message Dissemination Approach in Delay Tolerant Networks. In: International Conference on Future Network Systems and Security. Springer International Publishing, 2016. p. 62-80. [21] Bin Guo, Zhu Wang, Zhiwen Yu, Yu Wang, Neil Y. Yen, RUNHE HUANG, XINGSHE ZHOU. Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm. Journal ACM Computing Surveys (CSUR), Volume 48 Issue 1, September 2015 Article No. 7. [22] Rafael Laufer, Henri Dubois-Ferri"ere, Leonard Kleinrock. Multirate Anypath Routing in Wireless Mesh Networks. IEEE INFOCOM, 2009. [23] H. Li, T. Li, W. Wang and Y. Wang, "Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing," in IEEE Transactions on Mobile Computing. [24] Zhenyu Zhou, Haijun Liao, Bo Gu, Kazi Mohammed Saidul Huq, Shahid Mumtaz, and Jonathan Rodriguez. Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing. IEEE Network Volume: 32, Issue: 4, July/August 2018.
描述:	碩士國立政治大學資訊科學系 105753019
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105753019
数据类型:	thesis
DOI:	10.6814/THE.NCCU.CS.007.2019.B02
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
301901.pdf	2208Kb	Adobe PDF2	236	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈