Credibility Verification Mechanism for Remote Sensed Data in Mobile Crowdsensing System
管理學院 - 資訊管理系
Department of Information Management
|Thesis Publication Year:||2018|
|Graduation Academic Year:||106|
|Keywords (in Chinese):||群眾感知 、資料可信度 、模糊金庫機制|
|Keywords (in other languages):||Crowdsensing, Data Credibility, Fuzzy Vault|
|Reference times:||Clicks: 55 Downloads: 5|
|School Collection Retrieve National Library Collection Retrieve Error Report|
近年來大數據、雲端運算與行動應用等相關技術成熟，衍生出各式智能系統的 發展。當各式智能系統使用於社會公共議題中時，需倚靠蒐集大量的資料的環境資 料作為決策的基礎。若採用部署感測裝置的方式，會面臨部署以及營運維護等執行 問題，且缺乏高機動性。近年來群眾感知 (Crowdsensing) 概念逐漸興起，倚靠民眾 所具備的高機動性以及數量龐大等優點，可取得大量環境資料。然而在使用群眾所 提供的環境數據前須確保資料是否可信，若因採信了含有錯誤、造假的資料，導致 決策錯誤或處理失當，可能會造成莫大的損失，故如何有效的驗證民眾所搜集的資 料為可信賴的成為重要議題，本研究為了解決此問題，提出了一套驗證群眾感知資 料的框架，透過部署可涵蓋感測區域數量的感測器，利用感測器搜集環境數據搭配 模糊金庫機制 (F uzzy V ault Scheme) 的特性，即時驗證群眾感知資料可信賴性，進 而提升資料品質、節省成本以及解決公共議題的果效。
In recent years, the maturity of related technologies such as big data, cloud com- puting and mobile applications has promoted the development of various integrate systems. In all kinds of social public issues, it is necessary to rely on collecting a large amount of environmental data as a basis. However, if the method of deploying the sensing device is adopted, it may face implementation problems such as deploy- ment and operation and maintenance, and lack of high mobility. In recent years, the concept of Crowdsensing has gradually emerged, relying on the high mobility and large quantity of the people, and can obtain a large amount of environmental infor- mation. However, before using the environmental data provided by the masses, it is necessary to ensure that the information is credible. If the information is wrong or falsified, which leads to mistakes in decision-making or mishandling, it may cause great losses. Therefore, how to effectively verify the collected information. In order to solve this problem, this paper proposes a framework for verifying the perceptual data of the masses. By deploying a sensor that covers the number of sensing areas, the sensor is used to collect environmental data and match the Fuzzy Vault. The mathematical characteristics help to verify the trustworthiness of the mass percep- tion of the data, thereby improving the quality of the data, saving costs and solving the effects of public issues.
 V. Vescoukis, N. Doulamis, and S. Karagiorgou, “A service oriented architecture for decision support systems in environmental crisis management,” Future Generation Computer Systems, vol. 28, no. 3, pp. 593 – 604, 2012. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S0167739X11000380
 L.Xu,N.Liang,andQ.Gao,“Anintegratedapproachforagriculturalecosystemmanagement,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 4, pp. 590–599, July 2008.
 N.D.Lane,E.Miluzzo,H.Lu,D.Peebles,T.Choudhury,andA.T.Campbell,“Asurveyofmobile phone sensing,” IEEE Communications Magazine, vol. 48, no. 9, pp. 140–150, Sept 2010.
 P. Dutta, P. M. Aoki, N. Kumar, A. Mainwaring, C. Myers, W. Willett, and A. Woodruff, “Common sense: Participatory urban sensing using a network of handheld air quality monitors,” in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, ser. SenSys ’09. New York, NY, USA: ACM, 2009, pp. 349–350. [Online]. Available: http://doi.acm.org/10.1145/1644038.1644095
 B. Guo, Z. Wang, Z. Yu, Y. Wang, N. Y. Yen, R. Huang, and X. Zhou, “Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm,” ACM Comput. Surv., vol. 48, no. 1, pp. 7:1–7:31, Aug. 2015. [Online]. Available: http://doi.acm.org/10.1145/2794400
 R. K. Ganti, F. Ye, and H. Lei, “Mobile crowdsensing: current state and future challenges,” IEEE Communications Magazine, vol. 49, no. 11, pp. 32–39, November 2011.
 T. Zhou, Z. Cai, K. Wu, Y. Chen, and M. Xu, “Fidc: A framework for improving data credibility in mobile crowdsensing,” Computer Networks, vol. 120, pp. 157 – 169, 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1389128617301445
 T. Zhou, Z. Cai, M. Xu, and Y. Chen, “Leveraging crowd to improve data credibility for mo- bile crowdsensing,” in 2016 IEEE Symposium on Computers and Communication (ISCC), June 2016, pp. 561–568.
 T. Luo and L. Zeynalvand, “Reshaping mobile crowd sensing using cross validation to improve data credibility,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Dec 2017, pp. 1–7. P. Gilbert, L. P. Cox, J. Jung, and D. Wetherall, “Toward trustworthy mobile sensing,” in Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications, ser. HotMobile ’10. New York, NY, USA: ACM, 2010, pp. 31–36. [Online]. Available: http://doi.acm.org/10.1145/1734583.1734592
 U. Rührmair, J. L. Martinez-Hurtado, X. Xu, C. Kraeh, C. Hilgers, D. Kononchuk, J. J. Finley, and W. P. Burleson, “Virtual proofs of reality and their physical implementation,” in 2015 IEEE Symposium on Security and Privacy, May 2015, pp. 70–85.
 A. Juels and M. Sudan, “A fuzzy vault scheme,” in Proceedings IEEE International Symposium on Information Theory,, 2002, pp. 408–.
 C. Ellison, C. Hall, R. Milbert, and B. Schneier, “Protecting secret keys with personal entropy,” Future Gener. Comput. Syst., vol. 16, no. 4, pp. 311–318, Feb. 2000. [Online]. Available: http://dx.doi.org/10.1016/S0167-739X(99)00055-2
 D.BleichenbacherandP.Q.Nguyen,“Noisypolynomialinterpolationandnoisychineseremain- dering,” in Advances in Cryptology — EUROCRYPT 2000, B. Preneel, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 53–69.
 U.Uludag,S.Pankanti,andA.K.Jain,“Fuzzyvaultforfingerprints,”inAudio-andVideo-Based Biometric Person Authentication, T. Kanade, A. Jain, and N. K. Ratha, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 310–319.