Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/146803
|
Title: | 農業物聯網數據異常檢測的監督式學習演算法 A Supervised Learning Algorithm for Anomaly Detection in Agricultural Internet of Things Data |
Authors: | 王飛鴻 Wang, Fei-Hong |
Contributors: | 蔡瑞煌 周珮婷 Tsaih, Rua-Huan Chou, Pei-Ting 王飛鴻 Wang, Fei-Hong |
Keywords: | 農業物聯網 監督式學習演算法 數據異常檢測 Agricultural IoT Supervised learning algorithm Data anomaly detection |
Date: | 2023 |
Issue Date: | 2023-08-16 13:36:29 (UTC+8) |
Abstract: | 隨著物聯網、雲計算等資訊技術的發展普及,農業資訊化從數位農業、精準農業進入到了一個新的階段——智慧農業。智慧農業是以物聯網技術為主要支撐和手段的現代農業形態,它在受益於物聯網技術進步的同時,也不可避免的受制於農業物聯網的缺陷,尤其是土壤監測器、微氣象站、智能電網系統等物聯網感測器出現的異常情況。為了讓農業生產者大量購買使用,農業物聯網感測器往往在產品設計,用料,功能等方面盡可能降低成本,從而導致其品質和穩定性的降低,再加上長時間暴露在自然環境下,更容易發生異常甚至損壞……因此,物聯網感測器在日常運作過程中,難免會出現電池電量低,校準不當、設備老化等異常情況。而這種異常又往往會導致監測資料的異常,讓農業生產者做出錯誤的判斷,進行錯誤的決策,從而導致嚴重的後果。因此,我們迫切需要找到一種方法,能夠幫助農業生產者自動檢測農業物聯網的數據異常,並加以精准的分析,從而引導生產者做出科學的判斷。
在本研究中,我們提出了一種基於機器學習的監督式學習演算法,即 SH 學習演算法(Soil humidity learning algorithm, SHLA)來實現農業物聯網的數據異常檢測。 With the development of information technologies such as the Internet of Things(IoT), agricultural informatization has become smart agriculture. Different from past, smart agriculture is highly dependent on IoT sensors, such as soil monitors, microweather stations, smart grid systems, etc. However, while smart agriculture enjoys the benefits brought by the progress of IoT, it is also limited to the shortcomings of it. Under normal circumstances, it is inevitable for IoT sensors to encounter abnormal conditions such as low battery power, improper adjustment, or aging equipment. Especially agricultural IoT sensors’ product design, materials, functions, etc. have been weakened to reduce costs to help agricultural producers purchase or replace in large quantities, which means the stability of agricultural IoT sensors is greatly reduced. In addition, since agricultural IoT sensors are exposed to the natural environment, sensors are more prone to abnormalities or even get broken.
For agricultural producers, unstable agricultural IoT sensors with anomaly data, may make them make wrong decisions, resulting in serious consequences such as reduced crop yields even crop damage. Therefore, smart agriculture urgently needs a method that can automatically detect data anomalies in the agricultural IoT.
In this experiment, we propose a supervised learning algorithm named SHLA (Soil humidity learning algorithm) based on machine learning to detect the data anomalies of the agricultural IoT. |
Reference: | 陳隆昇&林立為. (2009).植基倒傳遞類神經網路之不平衡資料處理機制.
李道亮. (2012). 物聯網與智慧農業: 農業工程.
Adkisson, M., Kimmell, J. C., Gupta, M., & Abdelsalam, M. (2021). Autoencoder-based Anomaly Detection in Smart Farming Ecosystem. Paper presented at the 2021 IEEE International Conference on Big Data (Big Data).
Ahmed, N., De, D., & Hussain, I. (2018). Internet of Things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet of Things Journal, 5(6), 4890-4899.
Ali, Z. H., Ali, H. A., & Badawy, M. M. (2015). Internet of Things (IoT): definitions, challenges and recent research directions. International Journal of Computer Applications, 128(1), 37-47.
Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97-114.
Auskalnis, J., Paulauskas, N., & Baskys, A. (2018). Application of local outlier factor algorithm to detect anomalies in computer network. Elektronika ir Elektrotechnika, 24(3), 96-99.
Barber, D. (2012). Bayesian reasoning and machine learning: Cambridge University Press.
Barnett, V., & Lewis, T. (1984). Outliers in statistical data. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics.
Barreto, L., & Amaral, A. (2018). Smart farming: Cyber security challenges. Paper presented at the 2018 International Conference on Intelligent Systems (IS).
Bauer, J., & Aschenbruck, N. (2017). Measuring and adapting MQTT in cellular networks for collaborative smart farming. Paper presented at the 2017 IEEE 42nd conference on local computer networks (LCN).
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4): Springer.
Catalano, C., Paiano, L., Calabrese, F., Cataldo, M., Mancarella, L., & Tommasi, F. (2022). Anomaly detection in smart agriculture systems. Computers in Industry, 143, 103750.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
Chung, H. C., Kim, D. I., & Moon, A. K. (2015). Overview of Smart Farming based on networks. Paper presented at the Proceedings of the Korean Institute of Information and Commucation Sciences Conference.
Cook, A. A., Mısırlı, G., & Fan, Z. (2019). Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal, 7(7), 6481-6494.
Dagar, R., Som, S., & Khatri, S. K. (2018). Smart farming–IoT in agriculture. Paper presented at the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).
Elaoud, A., Hassen, H. B., Salah, N. B., Masmoudi, A., & Chehaibi, S. (2017). Modeling of soil penetration resistance using multiple linear regression (MLR). Arabian Journal of Geosciences, 10, 1-8.
Gluhak, A., Krco, S., Nati, M., Pfisterer, D., Mitton, N., & Razafindralambo, T. (2011). A survey on facilities for experimental internet of things research. IEEE Communications Magazine, 49(11), 58-67.
Hawkins, D. M. (1980). Identification of outliers (Vol. 11): Springer.
Janek Dabrowski, J., Rahman, A., Hellicar, A., Rana, M., & Arnold, S. (2022). Deep Learning for Prawn Farming: Forecasting and Anomaly Detection. arXiv e-prints, arXiv: 2205.06359.
Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012). Future internet: the internet of things architecture, possible applications and key challenges. Paper presented at the 2012 10th international conference on frontiers of information technology.
Kouadri, S., Pande, C. B., Panneerselvam, B., Moharir, K. N., & Elbeltagi, A. (2021). Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environmental Science and Pollution Research, 1-25.
Kraijak, S., & Tuwanut, P. (2015). A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends. Paper presented at the 2015 IEEE 16th International Conference on Communication Technology (ICCT).
KumarMahto, A., Biswas, R., & Alam, M. A. (2019). Short term forecasting of agriculture commodity price by using ARIMA: based on Indian market. Paper presented at the International Conference on Advances in Computing and Data Sciences.
Kusiak, A., Zheng, H., & Song, Z. (2009). Models for monitoring wind farm power. Renewable Energy, 34(3), 583-590.
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), 161-175.
Mohamudally, N., & Peermamode-Mohaboob, M. (2018). Building an anomaly detection engine (ade) for iot smart applications. Procedia computer science, 134, 10-17.
Moso, J. C., Cormier, S., de Runz, C., Fouchal, H., & Wandeto, J. M. (2021). Anomaly Detection on Data Streams for Smart Agriculture. Agriculture, 11(11), 1083.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective: MIT press.
Ou, C.-H., Chen, Y.-A., Huang, T.-W., & Huang, N.-F. (2020). Design and implementation of anomaly condition detection in agricultural IoT platform system. Paper presented at the 2020 International Conference on Information Networking (ICOIN).
Pukelsheim, F. (1994). The three sigma rule. The American Statistician, 48(2), 88-91.
Shi, J., He, G., & Liu, X. (2018). Anomaly detection for key performance indicators through machine learning. Paper presented at the 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC).
Singh, G., Sharma, D., Goap, A., Sehgal, S., Shukla, A., & Kumar, S. (2019). Machine Learning based soil moisture prediction for Internet of Things based Smart Irrigation System. Paper presented at the 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC).
Su, C.-T., Chen, L.-S., & Yih, Y. (2006). Knowledge acquisition through information granulation for imbalanced data. Expert Systems with applications, 31(3), 531-541.
Syafarinda, Y., Akhadin, F., Fitri, Z., Widiawan, B., & Rosdiana, E. (2018). The precision agriculture based on wireless sensor network with MQTT protocol. Paper presented at the IOP Conference Series: Earth and Environmental Science.
Tsai, F.-K., Chen, C.-C., Chen, T.-F., & Lin, T.-J. (2019). Sensor abnormal detection and recovery using machine learning for iot sensing systems. Paper presented at the 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA).
Zhang, K., Shi, S., Gao, H., & Li, J. (2007). Unsupervised outlier detection in sensor networks using aggregation tree. Paper presented at the Advanced Data Mining and Applications: Third International Conference, ADMA 2007 Harbin, China, August 6-8, 2007. Proceedings 3. |
Description: | 碩士 國立政治大學 資訊管理學系 110356050 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110356050 |
Data Type: | thesis |
Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
File |
Description |
Size | Format | |
605001.pdf | | 2329Kb | Adobe PDF2 | 15 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|