Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey
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The model in [129] achieved a good performance in terms of time inference and accuracy on two publicly agricultural datasets such as potato and tomato datasets.
“The model achieved a good performance in terms of time inference and accuracy on two publicly agricultural datasets such as, potato and tomato datasets.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [129]
Authors in [129] created a TinyML image classification deep neural network model deployed on smart camera to detect crop diseases.
“The authors in [129] created a TinyML image classification deep neural network model deployed on smart camera to detect crop diseases.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [129]
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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
- Authors
- Manokaran, J., & Vairavel, G.
- Affiliations
- SRM Institute of Science and Technology (IN)
- Corresponding author
- Manokaran, J.
- Venue
- Lecture Notes in Electrical Engineering
- Publisher
- Springer Singapore
- Published
- 2022
- Pages
- 685-702
- ISSN
- 1876-1100 (ISSN-L)
- 1876-1119
- OpenAlex
- W4226115281
- Cited by
- 13
- References
- 44
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 5 · 2022: 2
- FWCI
- 4.64
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in electrical engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- anomaly detection, Internet of Things, edge computing, data-driven methods, machine learning, Internet of Things security, network traffic monitoring, low-power devices, industrial automation, sensor data
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