A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation Article Swipe
Yan Zhong
,
Simon Fong
,
Shimin Hu
,
Raymond K. Wong
,
Weiwei Lin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19204536
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19204536
The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.
Related Topics
Concepts
Data mining
Computer science
Anomaly detection
Subspace topology
Granularity
Data stream mining
Data quality
Data processing
Similarity (geometry)
Internet of Things
Data collection
Artificial intelligence
Engineering
Database
Embedded system
Operations management
Image (mathematics)
Operating system
Statistics
Metric (unit)
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s19204536
- https://www.mdpi.com/1424-8220/19/20/4536/pdf?version=1571665621
- OA Status
- gold
- Cited By
- 15
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2980444234
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2980444234Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s19204536Digital Object Identifier
- Title
-
A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity TransformationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-18Full publication date if available
- Authors
-
Yan Zhong, Simon Fong, Shimin Hu, Raymond K. Wong, Weiwei LinList of authors in order
- Landing page
-
https://doi.org/10.3390/s19204536Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/19/20/4536/pdf?version=1571665621Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/19/20/4536/pdf?version=1571665621Direct OA link when available
- Concepts
-
Data mining, Computer science, Anomaly detection, Subspace topology, Granularity, Data stream mining, Data quality, Data processing, Similarity (geometry), Internet of Things, Data collection, Artificial intelligence, Engineering, Database, Embedded system, Operations management, Image (mathematics), Operating system, Statistics, Metric (unit), MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 3, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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