Cited by 21Water Systems and OptimizationNeural Networks and Reservoir ComputingAnomaly Detection Techniques and Applications

Online learning on tiny micro-controllers for anomaly detection in water distribution systems

Pau, Danilo, Khiari, Abderrahim, Denaro, Davide·2021·2021 IEEE 11th International Conference on Consumer Electronics (ICCE-Berlin)

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.

Integrity signals

Registry

Crossref · 10.1109/icce-berlin53567.2021.9720009

Cites

27 of 27 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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]

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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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