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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-Chiehen_US
dc.contributor.author (Authors) 徐紹鈞zh_TW
dc.contributor.author (Authors) Hsu, Shao-Chunen_US
dc.creator (作者) 徐紹鈞zh_TW
dc.creator (作者) Hsu, Shao-Chunen_US
dc.date (日期) 2017en_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) G0104753023en_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) 104753023zh_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 1
1.1 Background 1
1.1.1 SoundOfTheCity 2
1.1.2 DietSense 3
1.2 Motivation 4
1.3 Purpose 5
Chapter 2 Related Work 8
2.1 Rethinking routing information in mobile social networks: Location-based or social-based? 8
2.2 Plasma: A new routing paradigm for wireless multihop networks 10
2.3 A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks 11
Chapter 3 Mobile and Social Anypath Routing(M&SA) 13
3.1 Scenario 13
3.2 NCCU Trace 14
3.3 Forwarding Set 15
3.4 Formula Define 16
3.5 Link Cost 17
3.5.1 Contact Ratio 17
3.5.2 Social Stability 18
3.6 Remaining Cost 20
3.6.1 Centrality 21
Chapter 4 Simulation 24
4.1 Simulate Setup 25
4.2 Performance of Contact Type 26
4.3 Different Social Parameters 27
4.4 Performance Evaluation 29
Chapter 5 Conclusion 33
Reference 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/#G0104753023en_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 networken_US
dc.subject (關鍵詞) Social networken_US
dc.subject (關鍵詞) Social relationshipen_US
dc.subject (關鍵詞) Routing strategyen_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 networksen_US
dc.type (資料類型) thesisen_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