學術產出-Theses
Article View/Open
Publication Export
-
題名 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貢獻者 蔡子傑
Tsai, Tzu-Chieh
鄧皓
Teng, Hao關鍵詞 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 上傳時間 4-Mar-2019 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 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) 鄧皓 zh_TW dc.contributor.author (Authors) Teng, Hao en_US dc.creator (作者) 鄧皓 zh_TW dc.creator (作者) Teng, Hao en_US dc.date (日期) 2019 en_US dc.date.accessioned 4-Mar-2019 21:34:26 (UTC+8) - dc.date.available 4-Mar-2019 21:34:26 (UTC+8) - dc.date.issued (上傳時間) 4-Mar-2019 21:34:26 (UTC+8) - dc.identifier (Other Identifiers) G0105753019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122396 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 105753019 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents Chapter 1 Introduction 11.1 Background 11.2 Motivation 21.3 Concept of MCSC 31.4 Relay Node Set 5Chapter 2 Related Work 72.1 Social Trace data 72.1.1 Reality mining: MIT [11] 72.1.2 Cambridge [12] 82.1.3 UPB Trace 92.2 Delay Tolerance Network(DTN) 92.2.1 Ad-Hoc Network 102.2.2 Epidemic 112.2.3 Spray and Wait 112.2.4 Prophet 122.3 Mobile Crowd Sensing and Computing(MCSC) 122.3.1 A Neighbor Collaboration Mechanism for Mobile Crowd Sensing in Opportunistic Networks [14] 132.3.2 Using Crowdsourced Data in Location-based Social Networks to Explore Influence Maximization [15] 132.3.3 POST: Exploiting Dynamic Sociality for Mobile Advertising in Vehicular Networks [16] 142.3.4 Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing[23] 152.4 Edge Computing 152.4.1 The Fog Makes Sense: Enabling Social Sensing Services with Limited Internet Connectivity [17] 152.4.2 Comparison of Edge Computing Implementations: Fog Computing, Cloudlet and Mobile Edge Computing [18] 162.4.3 Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing[24] 172.5 NCCU Trace 182.5.1 Form and Participants 182.5.2 Interests 19Chapter 3 Our Approach 203.1 Message 203.2 Anypath routing 203.3 Edge node 213.4 Model 223.5 Upload Section 223.5.1 Bellman-Ford Algorithm 233.5.2 Cost and Probability 233.5.3 Relay Node Set 253.6 Download Section 263.6.1 Cosine Similarity 263.6.2 Possible Relay Cost 273.6.3 Influence Gain 283.7 Flow Chart 30Chapter 4 Simulation 314.1 Environment 314.2 Setting 324.3 Results 334.3.1 Latency 334.3.2 Hop Count 354.3.3 Delivery Ratio 364.3.4 Overhead Ratio 374.3.5 Threshold 384.3.6 Weekdays and Weekend 414.3.7 Spray and Wait 43Chapter 5 Conclusion and Future Work 48Reference 50 zh_TW dc.format.extent 2261547 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105753019 en_US dc.subject (關鍵詞) 耐延遲網路 zh_TW dc.subject (關鍵詞) 行動群眾感知與計算 zh_TW dc.subject (關鍵詞) 機會性行動網路 zh_TW dc.subject (關鍵詞) 個人興趣 zh_TW dc.subject (關鍵詞) 追蹤數據 zh_TW dc.subject (關鍵詞) Delay Tolerant Network en_US dc.subject (關鍵詞) Mobile crowd sensing and computing en_US dc.subject (關鍵詞) Opportunistic mobile networks en_US dc.subject (關鍵詞) Personal interests en_US dc.subject (關鍵詞) Trace data en_US dc.title (題名) 行動群眾感知與計算的訊息蒐集與散播機制 zh_TW dc.title (題名) Message Collection and Dissemination Approach Using Mobile Crowd Sensing and Computing en_US dc.type (資料類型) thesis en_US dc.relation.reference (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. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.CS.007.2019.B02 en_US
