政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142118

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113648/144635 (79%)
Visitors : 51690129 Online Users : 626

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/142118

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142118

Title:	物聯網異常診斷平台：以環境物聯網為例 Anomaly Diagnostic Platform for IoT: Using Environmental IoT as an Example
Authors:	黃彥魁 Huang, Yen-Kuei
Contributors:	沈錳坤黃彥魁 Huang, Yen-Kuei
Keywords:	物聯網異常診斷樣式探勘 Internet of Things(IoT) Anomaly diagnosis Pattern mining
Date:	2022
Issue Date:	2022-10-05 09:13:29 (UTC+8)
Abstract:	隨著網路的普及和感測器成本的降低，物聯網也逐漸興起。透過物聯網，可以監測不合預期的異常症狀。現有物聯網異常相關技術的研究，主要著重在異常事件的偵測，較少異常事件成因診斷的研究。導致異常事件的成因可能是觀測環境的異常或儀器設備本身的異常。針對異常成因的診斷，現有研究都集中在網路攻擊的異常事件。本論文以環境物聯網為例，研究物聯網異常事件診斷的方法。我們歸納整理空汙環境物聯網的異常症狀、時空線索與成因，提出物聯網異常事件診斷的方法與流程。根據我們所提出的診斷流程，設計實作一個異常事件診斷系統，提供使用者透過聚焦、歸納、對比，逐步地由眾多可能的線索中，探索出最可能導致異常事件的成因。本論文並以真實案例，實證我們所提出的系統可以協助使用者方便有效地找出異常成因。 With the spread of the internet and the cost reduction of sensors, the Internet of Things (IoT) became more popular. People can monitor and detect unexpected anomaly symptoms using IoT. Most of the existing research focuses on anomaly event detection while little research has been paid to the anomaly event diagnosis. Anomaly event may come from the deviation in environment or malfunction of devices themselves. Most current work on anomaly event diagnosis aim at the malicious attacks in IoT network. This thesis investigated the method of anomaly event diagnosis using environmental IoT as an example. We organized anomaly symptoms, temporal clues, spatial clues, and the root causes of anomaly events in environmental IoT for air pollution. This thesis also proposed the method and the procedure to diagnose anomaly events. According to the proposed procedure, this thesis designed and implemented an anomaly diagnosis system. The system provides the ability to focus, organize and compare the clues for anomaly diagnosis. It helps users to rule out unlikely root causes and explore possible root causes that triggered anomaly events. The proposed approach is demonstrated by real cases to show that the system could assist users to explore the root causes of anomaly events conveniently and effectively.
Reference:	[1]C. Tsai, C. Lai, M. Chiang, and L. T. Yang, “Data Mining for Internet of Things: A Survey,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 77-97, 2014. [2]F. Chen, P. Deng, J. Wan, D. Zhang, A. V. Vasilakos, and X. Rong, “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, 2015. [3]G. Atluri, A. Karpatne, and V. Kumar, “Spatio-Temporal Data Mining: A Survey of Problems and Methods,” ACM Computing Surveys, vol. 51, no. 4, pp. 1-41, 2018. [4]O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758-3773, 2018. [5]A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, 2014. [6]R. Jurdak, X. R. Wang, O. Obst, and P. Valencia, "Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies," Intelligence-Based Systems Engineering, Intelligent Systems Reference Library, pp. 309-325, 2011. [7]V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009. [8]V. P. Illiano and E. C. Lupu, “Detecting Malicious Data Injections in Wireless Sensor Networks: A Survey,” ACM Computing Surveys, vol. 48, no. 2, pp. 1-33, 2015. [9]A. A. Cook, G. Mısırlı, and Z. Fan, “Anomaly Detection for IoT Time-Series Data: A Survey,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494, 2020. [10]L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, and A. Liotta, “Smart Anomaly Detection in Sensor Systems: A Multi-perspective Review,” Information Fusion, vol. 67, pp. 64-79, 2021. [11]M. Fahim and A. Sillitti, “Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review,” IEEE Access, vol. 7, pp. 81664-81681, 2019. [12]M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches,” Internet of Things, vol. 7, 2019. [13]I. Alrashdi, A. Alqazzaz, E. Aloufi, R. Alharthi, M. Zohdy, and H. Ming, “AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, pp. 305-310, 2019. [14]Z. Chen, L. Tian, and C. Lin, "A Method for Detection of Anomaly Node in IOT," Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, pp. 777-784, 2015. [15]Z. Deng, D. Weng, J. Chen, R. Liu, Z. Wang, J. Bao, Y. Zheng, and Y. Wu, “AirVis: Visual Analytics of Air Pollution Propagation,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 800-810, 2020. [16]L. Chen, Y. Ho, H. Hsieh, S. Huang, H. Lee, and S. Mahajan, “ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 559-570, 2018. [17]A. Rodrigues, T. Camilo, J. S. Silva, and F. Boavida, “Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey,” Journal of Network and Systems Management, vol. 21, no. 3, pp. 408-452, 2013. [18]S. Chou, H. Yen, and A. Pang, “A REM-Enabled Diagnostic Framework in Cellular-Based IoT Networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5273-5284, 2019. [19]D. Rodenas-Herráiz, P. R. A. Fidler, T. Feng, X. Xu, S. Nawaz, and K. Soga, “A Handheld Diagnostic System for 6LoWPAN Networks,” in 2017 13th Annual Conference on Wireless On-demand Network Systems and Services, pp. 104-111, 2017. [20]A. Mahapatro and P. M. Khilar, “Fault Diagnosis in Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2000-2026, 2013. [21]D. Li, Y. Wang, J. Wang, C. Wang, and Y. Duan, “Recent Advances in Sensor Fault Diagnosis: A Review,” Sensors and Actuators A: Physical, vol. 309, 2020. [22]Z. Zhang, A. Mehmood, L. Shu, Z. Huo, Y. Zhang, and M. Mukherjee, “A Survey on Fault Diagnosis in Wireless Sensor Networks,” IEEE Access, vol. 6, pp. 11349-11364, 2018. [23]C. Wang, H. T. Vo, and P. Ni, “An IoT Application for Fault Diagnosis and Prediction,” in 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 726-731, 2015. [24]M. F. Goodchild, “Geographical Data Modeling,” Computers & Geosciences, vol. 18, no. 4, pp. 401-408, 1992. [25]R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994. [26]J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” ACM SIGMOD Record, vol. 29, no. 2, pp. 1-12, 2000. [27]H. Lu, L. Feng, and J. Han, “Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules,” ACM Transactions on Information Systems, vol. 18, no. 4, pp. 423-454, 2000. [28]D. Brélaz, “New Methods to Color the Vertices of a Graph,” Communications of the ACM, vol. 22, no. 4, pp. 251-256, 1979.
Description:	碩士國立政治大學資訊科學系 108753105
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108753105
Data Type:	thesis
DOI:	10.6814/NCCU202201643
Appears in Collections:	[資訊科學系] 學位論文

Files in This Item:

File	Description	Size	Format
310501.pdf		4942Kb	Adobe PDF2	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback