Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
| Autor: | Mehrzad Lavassani, Ulf Jennehag, Stefan Forsström, Tingting Zhang |
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| Rok vydání: | 2018 |
| Předmět: |
Data stream
IoT Computer science online learning Real-time computing data mining fog computing monitoring Cloud computing 02 engineering and technology Communications system Biochemistry Article Analytical Chemistry Data modeling 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation Network packet business.industry Node (networking) Communication Systems Testbed 020206 networking & telecommunications Atomic and Molecular Physics and Optics 020201 artificial intelligence & image processing business Wireless sensor network Kommunikationssystem |
| Zdroj: | Sensors; Volume 18; Issue 5; Pages: 1532 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s18051532 |
| Popis: | Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications. SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle) |
| Databáze: | OpenAIRE |
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