Online learning on tiny micro-controllers for anomaly detection in water distribution systems
Abstract
Researchers have developed novel approaches and algorithms to aid in the planning, design, and administration of water distribution systems (WDS) since the 1960s. While early research focused on simulating the hydraulic of such systems, the 1980s saw a shift in focus to water quality subjects. Recent study has delved deeper into system reliability and resilience. This paper introduces a resource constrained anomaly detector for WDS to monitor its reliability. The proposed solution uses Reservoir Computing (RC), in particular Deep Echo State Network (DeepESN), to achieve high accuracy with low complexity. Furthermore, thanks to the special properties of DeepESN, online learning capabilities of this model is implemented in the micro-controller (MCU) to ensure the continuous adaptability over time of the model to adapt to the time varying data distribution. In most cases, researchers have relied on either hypothetical water distribution systems or a handful of actual systems for use as benchmark test systems. Skoltech Anomaly Benchmark (SKAB) dataset was used to measure the performance of those models. It consists of several anomalies in a sensorized WDS. The online learning feature is proposed in two different approaches: single iteration and batch decomposition Which give options for different implementations depending on the application needs and the MCU memory constraints. The accuracy, complexity and robustness of the proposed models are comprehensively validated through comparative analysis of many sets of hyper-parameters.
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Authors in [137] compared two types of TinyML models, an autoencoder and a variational autoencoder for mechanical anomaly detection in washing machines using an MCU device (Arduino Nano 33 BLE Sense).
“The authors in [137] compared two types of TinyML models, an autoencoder and a variational autoencoder for mechanical anomaly detection in washing machines using an MCU device (Arduino Nano 33 BLE Sense).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [137]
The results in [137] show around 92% accuracy and 90% precision.
“The results show around 92% accuracy and 90% precision.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [137]
The autoencoder model in [137] was chosen due to its performance in terms of accuracy.
“The autoencoder model was chosen due to its performance in terms of accuracy.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [137]
Cites
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Sherstinsky, A. (2020) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2019.132306 · Cited by 5,680 · see citing works
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811 · see citing works
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956 · see citing works
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649 · see citing works
An experimental unification of reservoir computing methods
Verstraeten, D., Schrauwen, B., D’Haene, M., & Stroobandt, D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.003 · Cited by 1,135 · see citing works
Musa, T. H. A., & Bouras, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2102-4_36 · Cited by 958 · see citing works
Optimization and applications of echo state networks with leaky- integrator neurons
Jaeger, H., Lukoševičius, M., Popovici, D., & Siewert, U. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.016 · Cited by 948 · see citing works
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W. D., Ni, J., Zong, B., Chen, H., & Chawla, N. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011409 · Cited by 904 · see citing works
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Inoue, J., Yamagata, Y., Chen, Y., POSKITT, C. M., & Sun, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.149 · Cited by 372 · see citing works
A Survey of Predictive Maintenance: Systems, Purposes and ApproachesPreprint
Zhu, T., Ran, Y., Zhou, X. J., & Wen, Y. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.time.2024.1 · Cited by 357 · see citing works
Sagheer, A., & Kotb, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-55320-6 · Cited by 351 · see citing works
Q-statistic and T2-statistic PCA-based measures for damage assessment in structures
Delgado, L. E. M., Rodellar, J., Fernández, A., & Güemes, A. (2010) · Structural Health Monitoring · doi:10.1177/1475921710388972 · Cited by 240 · see citing works
Water Distribution Reliability: Analytical Methods
Wagner, J. M., Shamir, U. Y., & Marks, D. H. (1988) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)0733-9496(1988)114:3(253 · Cited by 234 · see citing works
Architectural and Markovian factors of echo state networks
Gallicchio, C., & Micheli, A. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.02.002 · Cited by 198 · see citing works
Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT
Luo, T., & Nagarajan, S. G. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422402 · Cited by 172 · see citing works
Research Database of Water Distribution System Models
Jolly, M. D., Lothes, A. D., Bryson, L. S., & Ormsbee, L. E. (2013) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000352 · Cited by 99 · see citing works
Selim, G. E. I., Hemdan, E. E., Shehata, A. M., & El-Fishawy, N. A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10354-1 · Cited by 76 · see citing works
Deep Learning Based Anomaly Detection in Water Distribution Systems
Qian, K., Jiang, J. S., Ding, Y., & Yang, S. (2020) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc48988.2020.9238099 · Cited by 33 · see citing works
Markovian Bias of Neural-based Architectures With Feedback Connections
Peter Tiňo, B., Hammer, B., & Mikael Bodén, A. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-73954-8_5 · Cited by 32 · see citing works
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring DataPreprint
Russo, S., Disch, A., Blumensaat, F., & Villez, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03843 · Cited by 28 · see citing works
SKAB - Skoltech Anomaly Benchmark
Katser, I. D., & Олегович, К. В. (2020) · Kaggle · doi:10.34740/kaggle/dsv/1693952 · Cited by 24 · see citing works
Monitoring water distribution systems: understanding and managing sensor networks
Ediriweera, D. D., & Marshall, I. W. (2010) · Drinking water engineering and science · doi:10.5194/dwes-3-107-2010 · Cited by 18 · see citing works
Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection
Ayyagari, S. S. D. P., Jones, R. D., & Weddell, S. J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319215 · Cited by 17 · see citing works
Implementation of multi-layer leaky integrator networks on a cellular processor array
Barr, D. R. W., Dudek, P., Chambers, J. M., & Gurney, K. R. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371190 · Cited by 10 · see citing works
Predescu, A., Mocanu, M., & Lupu, C. (2018) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2018.8516643 · Cited by 5 · see citing works
INDICATORS FOR LEAKAGE IN WATER DISTRIBUTION SYSTEM
Samudro, G., & Mangkoedihardjo, S. (2010) · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 28 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
AIfES: A Next-Generation Edge AI Framework
Wulfert, L., Kühnel, J., Krupp, L., Viga, J., Wiede, C., Gembaczka, P., & Grabmaier, A. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3355495 · Cited by 54 · see citing works
Souza, A. P., Oliveira, B. A., de Andrade, M. L. S. C., Starling, M. C. V. M., Pereira, A. H., Maillard, P., Nogueira, K., dos Santos, J. A., & Amorim, C. C. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165964 · Cited by 23 · see citing works
Zhang, R., Chen, J., Song, Y., Shan, W., Chen, P., & Xia, Y. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02204-9 · Cited by 20 · see citing works
Awaisi, K. S., Ye, Q., & Sampalli, S. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3579750 · Cited by 15 · see citing works
Application of Tiny Machine Learning in Predicative Maintenance in Industries
Ooko, S. O., & Karume, S. M. (2024) · Journal of Computing Theories and Applications · doi:10.62411/jcta.10929 · Cited by 16 · see citing works
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
Ayaz, F., Zakariyya, I., Cano, J., Keoh, S. L., Singer, J., Pau, D. P., & Kharbouche-Harrari, M. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191429 · Cited by 11 · see citing works
Self-citationAutomated Neural and On-Device Learning for Micro Controllers
Pau, D. P., & Ambrose, P. K. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9843050 · Cited by 10 · see citing works
Self-citationA Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices
Pau, D. P., Ambrose, P. K., & Aymone, F. M. (2023) · Chips · doi:10.3390/chips2020008 · Cited by 11 · see citing works
Self-citationLee, Y., Song, S., Park, K.-Y., Koo, B.-M., & Baek, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3507042 · Cited by 6 · see citing works
TinyRCE: Forward Learning Under Tiny Constraints
Pau, D. P., Ambrose, P. K., Pisani, A., & Aymone, F. M. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405784 · Cited by 5 · see citing works
Self-citationEfficient On-Device Domain Learning for Keyword Spotting on Ultra-Low-Power Platforms
Cioflan, C., Cavigelli, L., Rusci, M., de Prado, M., & Luca Benini, C. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3654437 · Cited by 3 · see citing works
Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams
Jakob, J., Artelt, A., Hasenjäger, M., & Hammer, B. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2198846 · Cited by 2 · see citing works
Identifying Anomalies in Water Pump Systems Using Machine Learning and an Integrated Ensemble Method
Sahoo, S. S., Singh, A., & Kumari, M. (2024) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis61070.2024.10594390 · Cited by 2 · see citing works
Detecting unregistered users through semi-supervised anomaly detection with similarity datasets
Heo, D. H., Park, S. H., & Kang, S. J. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00791-8 · Cited by 2 · see citing works
Emerging application use cases and future directions
Ghorpade, S. N., & Chougule, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00024-5 · Cited by 1 · see citing works
Nivarthi, C. P., Huang, Z., Gruhl, C., & Sick, B. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651344 · Cited by 1 · see citing works
Albaiz, A., & Amsaad, F. (2026) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc69565.2026.11542250 · Cited by 1 · see citing works
A Transfer Learning-Based New User Recognition for Minimizing Retraining Time in Edge Deep Learning
Heo, D. H., & Kang, S. J. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00031
Albaiz, A., & Amsaad, F. (2026) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc69565.2026.11542487
Anomaly detection system at the edge of the internet based on the MPT-FLA
Zunic, A., Vasiljevic, P., & Popović, M. (2026) · Zooming Innovation in Consumer Technologies Conference (ZINC) · doi:10.1109/zinc69910.2026.11655795
- Authors
- Pau, D., Khiari, A., & Denaro, D.
- Affiliations
- STMicroelectronics (Italy) (IT)
- University of Padua (IT)
- Venue
- 2021 IEEE 11th International Conference on Consumer Electronics (ICCE-Berlin)
- Publisher
- IEEE
- Published
- 2021-11-15
- Pages
- 1-6
- OpenAlex
- W4214601645
- Cited by
- 21
- References
- 28
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 6 · 2023: 9 · 2022: 1
- FWCI
- 11.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Consumer Electronics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- water distribution systems, anomaly detection, online learning, deep echo state network, reservoir computing, microcontroller, resource-constrained systems, system reliability
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