IoT anomaly detection methods and applications: A survey
Open accessAbstract
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.
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The team in PlantVillage [130] created the Nuru app to aid farmers in identifying and combating plant diseases.
“The team in this project [130] has created the Nuru app to aid farmers in identifying and combating plant diseases.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [130]
Our copy discusses IoT anomaly-detection applications, including smart farming in Section 2.1, but does not mention PlantVillage or the Nuru app. Searches for PlantVillage, Nuru, and the app’s claimed purpose found no passage addressing the claim. (LLM, conf=0.74)
Experimental — accuracy not yet measuredPlantVillage [130] is an open-source project managed by Penn State University.
“PlantVillage [130] is one of these chances.It is an open-source project managed by Penn State University.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [130]
Our copy’s sections on IoT anomaly-detection frameworks (4.8 and 4.11; page numbers unavailable) do not address PlantVillage. Searches for “PlantVillage” and “PlantVillage Penn State University open-source project” found no passage establishing that it is open-source or managed by Penn State University. (LLM, conf=0.76)
Experimental — accuracy not yet measuredBy using ML through TensorFlow Lite on mobile phones, the Nuru app provides real-time solutions without internet access, which is crucial for farmers in remote areas.
“By using ML through TensorFlow Lite on mobile phones, the app provides real-time solutions without internet access, which is crucial for farmers in remote areas.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [130]
The survey’s section 4.10 discusses on-device processing, but not the Nuru app. Searches for Nuru, TensorFlow Lite, offline mobile use, and farmers found no passage in our copy addressing the attributed claim; the retrieved real-time applications concern other systems. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Chatterjee, A., & Ahmed, B. S.
- Affiliations
- Karlstad University (SE)
- Corresponding author
- Ahmed, Bestoun S.
- Venue
- Internet of Things
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 19
- Pages
- 100568
- ISSN
- 2542-6605 (ISSN-L)
- OpenAlex
- W4285082635
- Cited by
- 323
- References
- 120
- Citations by year
- 2026: 70 · 2025: 100 · 2024: 99 · 2023: 51 · 2022: 3
- FWCI
- 20.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Internet of Things (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- IoT anomaly detection, anomaly detection algorithms, Internet of Things security, sensor-based monitoring, home automation, smart cities, data drift, concept drift, data augmentation, ground truth, multi-sensor integration
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