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題名 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作者 徐紹鈞
Hsu, Shao-Chun貢獻者 蔡子傑
Tsai, Tzu-Chieh
徐紹鈞
Hsu, Shao-Chun關鍵詞 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日期 2017 上傳時間 13-Sep-2017 14:49:34 (UTC+8) 摘要 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.參考文獻 [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. 描述 碩士
國立政治大學
資訊科學學系
104753023資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104753023 資料類型 thesis dc.contributor.advisor 蔡子傑 zh_TW dc.contributor.advisor Tsai, Tzu-Chieh en_US dc.contributor.author (Authors) 徐紹鈞 zh_TW dc.contributor.author (Authors) Hsu, Shao-Chun en_US dc.creator (作者) 徐紹鈞 zh_TW dc.creator (作者) Hsu, Shao-Chun en_US dc.date (日期) 2017 en_US dc.date.accessioned 13-Sep-2017 14:49:34 (UTC+8) - dc.date.available 13-Sep-2017 14:49:34 (UTC+8) - dc.date.issued (上傳時間) 13-Sep-2017 14:49:34 (UTC+8) - dc.identifier (Other Identifiers) G0104753023 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112680 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 104753023 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents Chapter 1 Introduction 11.1 Background 11.1.1 SoundOfTheCity 21.1.2 DietSense 31.2 Motivation 41.3 Purpose 5Chapter 2 Related Work 82.1 Rethinking routing information in mobile social networks: Location-based or social-based? 82.2 Plasma: A new routing paradigm for wireless multihop networks 102.3 A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks 11Chapter 3 Mobile and Social Anypath Routing(M&SA) 133.1 Scenario 133.2 NCCU Trace 143.3 Forwarding Set 153.4 Formula Define 163.5 Link Cost 173.5.1 Contact Ratio 173.5.2 Social Stability 183.6 Remaining Cost 203.6.1 Centrality 21Chapter 4 Simulation 244.1 Simulate Setup 254.2 Performance of Contact Type 264.3 Different Social Parameters 274.4 Performance Evaluation 29Chapter 5 Conclusion 33Reference 34 zh_TW dc.format.extent 951008 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104753023 en_US dc.subject (關鍵詞) 機會性行動網路 zh_TW dc.subject (關鍵詞) 社群網路 zh_TW dc.subject (關鍵詞) 社群關係 zh_TW dc.subject (關鍵詞) 路由策略 zh_TW dc.subject (關鍵詞) Opportunistic network en_US dc.subject (關鍵詞) Social network en_US dc.subject (關鍵詞) Social relationship en_US dc.subject (關鍵詞) Routing strategy en_US dc.title (題名) 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 zh_TW dc.title (題名) A message routing strategy for mobile crowd sensing and computing in opportunistic mobile and social networks en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [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. zh_TW
