IJAT Vol.18 No.2 pp. 302-315
doi: 10.20965/ijat.2024.p0302

Research Paper:

Unsupervised Anomaly Detection for IoT-Driven Multivariate Time Series on Moringa Leaf Extraction

Kurnianingsih*,† ORCID Icon, Retno Widyowati** ORCID Icon, Achmad Fahrul Aji* ORCID Icon, Eri Sato-Shimokawara*** ORCID Icon, Takenori Obo*** ORCID Icon, and Naoyuki Kubota*** ORCID Icon

*Department of Electrical Engineering, Politeknik Negeri Semarang
Jl. Prof. H. Soedarto SH, Tembalang, Semarang, Jawa Tengah 50275, Indonesia

Corresponding author

**Department of Pharmaceutical Sciences, Faculty of Pharmacy, Universitas Airlangga
Surabaya, Indonesia

***Faculty of Systems Design, Tokyo Metropolitan University
Hino, Japan

July 26, 2023
October 25, 2023
March 5, 2024
moringa leaf extraction, IoT, unsupervised anomaly detection, multivariate time series

The extraction of valuable compounds from moringa plants involves complex processes that are highly dependent on various environmental and operational factors. Monitoring these processes using Internet of Things (IoT)-based multivariate time series data presents a unique opportunity for improving efficiency and quality control. Multivariate time series data, characterized by multiple variables recorded over time, provides valuable insights into the behavior, interactions, and dependencies among different components within a system. However, with the increasing complexity and volume of IoT data generated during moringa extraction, the anomaly detection becomes challenging. The objective of this study is to develop a robust and efficient system capable of automatically detecting anomalous patterns in real time, providing early warning signals to operators, and facilitating timely interventions. This paper proposes a novel hybrid unsupervised anomaly detection model combining density-based spatial clustering of applications with noise and k-nearest neighbors for IoT-based multivariate time series data. We conducted extensive experiments on real-world moringa extraction, demonstrating the effectiveness and practicality of our proposed approach. In comparison to other anomaly detection methods, our proposed method has the highest precision value of 0.89, the highest recall value of 0.89, and the highest accuracy value of 0.87. Future research will measure and optimize actuators (relays and motors) from anomaly detection to action. It can also be used with forecasting algorithms to detect anomalies in the coming minutes.

Cite this article as:
Kurnianingsih, R. Widyowati, A. Aji, E. Sato-Shimokawara, T. Obo, and N. Kubota, “Unsupervised Anomaly Detection for IoT-Driven Multivariate Time Series on Moringa Leaf Extraction,” Int. J. Automation Technol., Vol.18 No.2, pp. 302-315, 2024.
Data files:
  1. [1] D. Kokuryo, T. Kaihara, S. S. Kuik, S. Suginouchi, and K. Hirai, “Value Co-Creative Manufacturing with IoT-Based Smart Factory for Mass Customization,” Int. J. Automation Technol., Vol.11, No.3, pp. 509-518, 2017.
  2. [2] A. Iqbal, N. S. Mian, A. Longstaff, and S. Fletcher, “Performance Evaluation of Low-Cost Vibration Sensors in Industrial IoT Applications,” Int. J. Automation Technol., Vol.16, No.3, pp. 329-339, 2022.
  3. [3] J.-C. Tu, C.-H. Yang, and Y.-Y. Chen, “Exploring the Impact of IoT and Green Advertising on Consumer Behavior,” Int. J. Automation Technol., Vol.16, No.6, pp. 795-806, 2022.
  4. [4] N. N. Misra, Y. Dixit, A. Al-Mallahi, M. S. Bhullar, R. Upadhyay, and A. Martynenko, “IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry,” IEEE Internet Things J., Vol.9, No.9, pp. 6305-6324, 2022.
  5. [5] T. Anand, S. Sinha, M. Mandal, V. Chamola, and F. R. Yu, “AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture,” IEEE Sens. J., Vol.21, No.16, pp. 17581-17590, 2021.
  6. [6] R. K. Saini, I. Sivanesan, and Y.-S. Keum, “Phytochemicals of Moringa oleifera: A review of their nutritional, therapeutic and industrial significance,” 3 Biotech, Vol.6, No.2, Article No.203, 2016.
  7. [7] S. L. Rodríguez De Luna, R. E. Ramírez-Garza, and S. O. Serna Saldívar, “Environmentally Friendly Methods for Flavonoid Extraction from Plant Material: Impact of Their Operating Conditions on Yield and Antioxidant Properties,” Sci. World J., Vol.2020, Article No.6792069, 2020.
  8. [8] D. Cavaliere and S. Senatore, “Incremental Knowledge Extraction from IoT-Based System for Anomaly Detection in Vegetation Crops,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol.15, pp. 876-888, 2022.
  9. [9] H. Yang, S. Li, L. Tu, R. Ma, and Y. Chen, “Unsupervised Outlier Detection Mechanism for Tea Traceability Data,” IEEE Access, Vol.10, pp. 94818-94831, 2022.
  10. [10] J. Hu, K. Kaur, H. Lin, X. Wang, M. M. Hassan, I. Razzak, and M. Hammoudeh, “Intelligent Anomaly Detection of Trajectories for IoT Empowered Maritime Transportation Systems,” IEEE Trans. Intell. Transp. Syst., Vol.24, No.2, pp. 2382-2391, 2023.
  11. [11] Kurnianingsih, L. E. Nugroho, Widyawan, L. Lazuardi, A. S. Prabuwono, and M. Pratama, “Anomaly detection for elderly home care,” Int. J. Bus. Intell. Data Min., Vol.16, No.4, pp. 418-444, 2020.
  12. [12] D. Tang, Y. Yoshihara, T. Obo, T. Takeda, J. Botzheim, and N. Kubota, “Evolution strategy for anomaly detection in daily life monitoring of elderly people,” 2016 55th Annu. Conf. Soc. Instrum. Control Eng. Jpn. (SICE), pp. 1376-1381, 2016.
  13. [13] J. Zhang, Y. Xie, G. Pang, Z. Liao, J. Verjans, W. Li, Z. Sun, J. He, Y. Li, C. Shen, and Y. Xia, “Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection,” IEEE Trans. Med. Imaging, Vol.40, No.3, pp. 879-890, 2021.
  14. [14] O. Salem, K. Alsubhi, A. Mehaoua, and R. Boutaba, “Markov Models for Anomaly Detection in Wireless Body Area Networks for Secure Health Monitoring,” IEEE J. Sel. Areas Commun., Vol.39, No.2, pp. 526-540, 2021.
  15. [15] Y. Kondo and Y. Miyake, “A Study on Anomaly Detection of Water-Soluble Coolant Using Internal-Sensors,” Int. J. Automation Technol., Vol.16, No.2, pp. 175-181, 2022.
  16. [16] X. Zhou, Y. Hu, W. Liang, J. Ma, and Q. Jin, “Variational LSTM Enhanced Anomaly Detection for Industrial Big Data,” IEEE Trans. Ind. Inform., Vol.17, No.5, pp. 3469-3477, 2021.
  17. [17] J. Cai, Q. Wang, J. Luo, Y. Liu, and L. Liao, “CapBad: Content-Agnostic, Payload-Based Anomaly Detector for Industrial Control Protocols,” IEEE Internet Things J., Vol.9, No.14, pp. 12542-12554, 2022.
  18. [18] W. Jiang, Y. Hong, B. Zhou, X. He, and C. Cheng, “A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series,” IEEE Access, Vol.7, pp. 143608-143619, 2019.
  19. [19] H. Zeng, X. Zhao, and L. Wang, “Multivariate Time Series Anomaly Detection on Improved HTM Model,” 2021 IEEE Int. Conf. Comput. Sci. Electron. Inf. Eng. Intell. Control Technol. (CEI), pp. 759-763, 2021.
  20. [20] H. Nizam, S. Zafar, Z. Lv, F. Wang, and X. Hu, “Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT,” IEEE Sens. J., Vol.22, No.23, pp. 22836-22849, 2022.
  21. [21] Z. Chen, D. Chen, X. Zhang. Z. Yuan, and X. Cheng, “Learning Graph Structures with Transformer for Multivariate Time-Series Anomaly Detection in IoT,” IEEE Internet Things J., Vol.9, No.12, pp. 9179-9189, 2022.
  22. [22] R. Khilar, K. Mariyappan, M. S. Christo, J. Amutharaj, T. Anitha, T. Rajendran, and A. Batu, “Artificial Intelligence-Based Security Protocols to Resist Attacks in Internet of Things,” Wirel. Commun. Mob. Comput., Vol.2022, Article No.1440538, 2022.
  23. [23] Y. Wu, H.-N. Dai, and H. Tang, “Graph Neural Networks for Anomaly Detection in Industrial Internet of Things,” IEEE Internet Things J., Vol.9, No.12, pp. 9214-9231, 2022.
  24. [24] K. DeMedeiros, A. Hendawi, and M. Alvarez, “A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks,” Sensors, Vol.23, No.3, Article No.1352, 2023.
  25. [25] G. S. Fuhnwi, J. O. Agbaje, K. Oshinubi, and O. J. Peter, “An Empirical Study on Anomaly Detection Using Density-Based and Representative-Based Clustering Algorithms,” J. Niger. Soc. Phys. Sci., Article No.1364, 2023.
  26. [26] B. Zou, K. Yang, X. Kui, J. Liu, S. Liao, and W. Zhao, “Anomaly Detection for Streaming Data Based on Grid-Clustering and Gaussian Distribution,” Inf. Sci., Vol.638, Article No.118989, 2023.
  27. [27] H. Ghamkhar, M. J. Ghazizadeh, S. H. Mohajeri, I. Moslehi, and E. Yousefi-Khoshqalb, “An Unsupervised Method to Exploit Low-Resolution Water Meter Data for Detecting End-Users with Abnormal Consumption: Employing the DBSCAN and Time Series Complexity,” Sustain. Cities Soc., Vol.94, Article No.104516, 2023.
  28. [28] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proc. 2nd Int. Conf. Knowl. Discov. Data Min. (KDD’96), pp. 226-231 1996.
  29. [29] A. Mucherino, P. J. Papajorgji, and P. M. Pardalos, “k-Nearest Neighbor Classification,” A. Mucherino, P. J. Papajorgji, and P. M. Pardalos, “Data Mining in Agriculture,” pp. 83-106, Springer, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024