Decision tree and random forest implementations for fast filtering of sensor data
Abstract
With increasing capabilities of energy efficient systems, computational technology can be deployed, virtually everywhere. Machine learning has proven a valuable tool for extracting meaningful information from measured data and forms one of the basic building blocks of ubiquitous computing. In high-throughput applications, measurements are rapidly taken to monitor physical processes. This brings modern communication technologies to its limits. Therefore, only a subset of measurements, the interesting ones, should be further processed and possibly communicated to other devices. In this paper, we investigate architectural characteristics of embedded systems for filtering high-volume sensor data before further processing. In particular, we investigate implementations of decision trees and random forests for the classical von-Neumann computing architecture and custom circuits by the means of field programmable gate arrays.
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They do not address the ML model deployment and versioning challenges for online data cleaning.
“They do not address the ML model deployment and versioning challenges for online data cleaning.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
We identified five online cleaning techniques [5,8,29,40,48,49].
“We identified five online cleaning techniques [5,8,29,40,48,49].”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
Four of them [5,42,45,48] use filtering to clean noise.
“Four of them [5,42,45,48] use filtering to clean noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
Five primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.
“Five primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
Two online data cleaning techniques use ML solutions (DBSCAN and decision trees and random forest classification [5]).
“Two online data cleaning techniques use ML solutions (DBSCAN -an unsupervised learning method utilized in ML algorithms [40] and decision trees and random forest classification [5]).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
Six primary studies [5,7,11,13,21,41] focus on data quality management in edge-based architecture levels (L2 and L3).
“Six primary studies [5,7,11,13,21,41] focus on data quality management in edge-based architecture levels (L2 and L3).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
Only one study [5] provides a data quality technique running on edge (L3).
“Only one study [5] provides a data quality technique running on edge (L3).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [5]
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 68 · see citing works
- Authors
- Buschjager, S., & Morik, K.
- Affiliations
- TU Dortmund University (DE)
- Venue
- IEEE Transactions on Circuits and Systems I Regular Papers
- Published
- 2018-01
- Volume
- 65
- Issue
- 1
- Pages
- 209-222
- ISSN
- 1549-8328 (ISSN-L)
- 1558-0806
- OpenAlex
- W2733186554
- Cited by
- 78
- References
- 29
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 11 · 2023: 11 · 2022: 13 · 2021: 12 · 2020: 11 · 2019: 2 · 2018: 5
- FWCI
- 5.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Circuits and Systems I Regular Papers (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Embedded Systems and FPGA Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Embedded Systems and FPGA Applications
- Keywords
- decision tree, random forest, sensor data filtering, embedded systems, FPGA implementation, energy-efficient computing, high-throughput data processing, von Neumann architecture, machine learning hardware
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