政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/112680

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/112680

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/112680

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112680

Title:	結合機會性行動與社群網路的群眾行動感測與計算之訊息路由策略 A message routing strategy for mobile crowd sensing and computing in opportunistic mobile and social networks
Authors:	徐紹鈞 Hsu, Shao-Chun
Contributors:	蔡子傑 Tsai, Tzu-Chieh 徐紹鈞 Hsu, Shao-Chun
Keywords:	機會性行動網路社群網路社群關係路由策略 Opportunistic network Social network Social relationship Routing strategy
Date:	2017
Issue Date:	2017-09-13 14:49:34 (UTC+8)
Abstract:	由於各式行動裝置數量的大幅增加，使得透過攜帶這些裝置的行動群眾，來感測與計算的應用也隨之發展。行動感測與計算，重要的關鍵因素，就是需要群眾的參與與互動，以提高資料品質，並透過異質性網路的結合，以善用裝置的資源，提升資料傳輸的效率，才能符合及時資料分析與探勘的應用需求。本論文以實際狀況為考量，並加入社群網路以增加群眾參與意願，提出了一個結合機會性行動與社群網路的架構，及路由策略，期能解決行動群眾感測與計算情境下，感測資料傳輸的效率問題。此路由策略，乃透過隨意路線概念，基於此架構下，紀錄最近相遇的節點集合，並結合社群關係，以計算傳輸成本。在我們的效能評估中，我們提出的方法比起其他方法，其整體表現在增加適當成本下，能有效提升訊息傳達成功的機率與降低傳送的延遲時間。 The surging of various mobile devices leads to vigorous growth of Mobile Crowd Sensing and Computing (MCSC) applications. The key factors of MCSC are the participation and interaction of users carrying these devices to improve data quality, and efficient usage of the device resources by using heterogeneous networks’ connections to increase efficiency of transmission. It will thus meet the application requirements of in time data analysis and mining. In this research, we consider the network situation in daily life, with adding the social network for increasing incentive. We propose an integrated network with opportunistic mobile and social networks, and based on that, a message routing strategy to deal with the transmission efficiency problem for MCSC. The message routing strategy is modified from Anypath routing. It records the node met recently and the social relationship to evaluate transmission cost. In the performance evaluation, compared to other strategies, the simulation results show that our proposed strategy can successfully enhance messages delivery ratio and reduce transmission delay with acceptable overhead increase.
Reference:	[1] MA, Huadong; ZHAO, Dong; YUAN, Peiyan. Opportunities in mobile crowd sensing. Communications Magazine, IEEE, 2014, 52.8: 29-35. [2] WEI, Ling-Yin; ZHENG, Yu; PENG, Wen-Chih. Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012. p. 195-203. [3] Bin Guo, Zhiwen Yu, Xingshe Zhou, and Daqing Zhang. 2014d. From participatory sensing to Mobile Crowd Sensing. In Proceedings of PERCOM Workshops. 593–598. [4] 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. [5] N.D. Lane, “Community-Aware Smartphone Sensing Systems,” IEEE Internet Computing, vol. 16, no. 3, 2012, pp. 60-64 [6] GUO, Bin, et al. From participatory sensing to mobile crowd sensing. In: Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on. IEEE, 2014. p. 593-598. [7] HIGUCHI, Tatsuro, et al. A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks. In: Communications (ICC), 2014 IEEE International Conference on. IEEE, 2014. p. 42-47. [8] WANG, Xiaofei, et al. TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks. In: INFOCOM, 2014 Proceedings IEEE. IEEE, 2014. p. 2346-2354. [9] GAO, Wei; CAO, Guohong. User-centric data dissemination in disruption tolerant networks. In: INFOCOM, 2011 Proceedings IEEE. IEEE, 2011. p. 3119-3127. [10] TSAI, Tzu-Chieh; CHAN, Ho-Hsiang. NCCU Trace: social-network-aware mobility trace. Communications Magazine, IEEE, 2015, 53.10: 144-149. [11] ZHU, Konglin; LI, Wenzhong; FU, Xiaoming. Rethinking routing information in mobile social networks: Location-based or social-based?. Computer Communications, 2014, 42: 24-37. [12] L. Freeman, Centrality in social networks: conceptual clarification, Social Networks 1 (3) (1979) 215–239. [13] N. Ristanovic, G. Theodorakopoulos, J.-Y. Le Boudec, Traps and pitfalls of using contact traces in performance studies of opportunistic networks, in:INFOCOM’12, 2012. [14] LAUFER, Rafael, et al. Plasma: A new routing paradigm for wireless multihop networks. In: INFOCOM, 2012 Proceedings IEEE. IEEE, 2012. p. 2706-2710. [15] MUN, Min, et al. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 2009. p. 55-68. [16] XIANG, Chaocan, et al. Passfit: Participatory sensing and filtering for identifying truthful urban pollution sources. Sensors Journal, IEEE, 2013, 13.10: 3721-3732. [17] 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. [18] WEPPNER, Jens, et al. Participatory Bluetooth scans serving as urban crowd probes. Sensors Journal, IEEE, 2014, 14.12: 4196-4206. [19] M. Vukovic, “Crowdsourcing for Enterprises,” Proc. of the 2009 IEEE Congress on Services, 2009, pp. 686-692. [20] JUNG, Yeonsu; BAEK, Yunju. Multi-hop data forwarding method for crowd sensing networks. Peer-to-Peer Networking and Applications, 2015, 1-12. [21] CHEN, Pin-Yu, et al. When crowdsourcing meets mobile sensing: a social network perspective. Communications Magazine, IEEE, 2015, 53.10: 157-163. [22] WEPPNER, Jens; LUKOWICZ, Paul. Bluetooth based collaborative crowd density estimation with mobile phones. In: Pervasive computing and communications (PerCom), 2013 IEEE international conference on. IEEE, 2013. p. 193-200. [23] WEI, Ling-Yin; ZHENG, Yu; PENG, Wen-Chih. Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012. p. 195-203. [24] Bin Guo, Zhiwen Yu, Xingshe Zhou, and Daqing Zhang. 2014d. From participatory sensing to Mobile Crowd Sensing. In Proceedings of PERCOM Workshops. 593–598.
Description:	碩士國立政治大學資訊科學學系 104753023
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0104753023
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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