RECORD
Machine Learning in the Internet of Things for Industry 4.0
Networking and Internet Architecture · Submitted 22 May 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow programming. It enables the development of reactive software with short processing and rapid response times, especially when moved to the edge of the network. This is especially important in systems that utilize online machine learning algorithms to analyze ongoing processes such as those observed in Industry 4.0. In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud. In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system. Finally, we are providing recommendations which machine learning patterns should be used in the IoT systems depending on the application type.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. | background 1.00 | 0.08 | 0.02 | asserted 1.00 |
| s1 | One of the approach to process sensor data is dataflow programming. | background 0.96 | 0.05 | 0.02 | asserted 0.96 |
| s2 | It enables the development of reactive software with short processing and rapid response times, especially when moved to the edge of the network. | background 0.97 | 0.09 | 0.03 | asserted 0.98 |
| s3 | This is especially important in systems that utilize online machine learning algorithms to analyze ongoing processes such as those observed in Industry 4.0. | background 0.99 | 0.07 | 0.05 | asserted 0.97 |
| s4 | In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. | contribution 0.58 | 0.74 | 0.34 | asserted 1.00 |
| s5 | We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud. | contribution 0.99 | 0.89 | 0.35 | asserted 0.78 |
| s6 | In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system. | contribution 0.89 | 0.85 | 0.05 | asserted 0.88 |
| s7 | Finally, we are providing recommendations which machine learning patterns should be used in the IoT systems depending on the application type. | contribution 0.85 | 0.82 | 0.22 | asserted 0.93 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow programming. It enables the development of reactive software with short processing and rapid response times, especially when moved to the edge of the network. This is especially important in systems that utilize online machine learning algorithms to analyze ongoing processes such as those observed in Industry 4.0. In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud. In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system. Finally, we are providing recommendations which machine learning patterns should be used in the IoT systems depending on the application type.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.