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Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informationssystem och -teknologi. (Communication Systems and Network (CSN))ORCID-id: 0000-0001-5808-1382
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informationssystem och -teknologi. (Communication Systems and Network (CSN))ORCID-id: 0000-0002-1797-1095
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informationssystem och -teknologi. (Communication Systems and Network (CSN))
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informationssystem och -teknologi. (Communication Systems and Network (CSN))
2018 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, nr 5, artikkel-id 1532Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
MDPI , 2018. Vol. 18, nr 5, artikkel-id 1532
Emneord [en]
data mining; fog computing; IoT; online learning; monitoring
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-33609DOI: 10.3390/s18051532OAI: oai:DiVA.org:miun-33609DiVA, id: diva2:1205328
Forskningsfinansiär
Knowledge Foundation, 20150363, 20140321, and 20140319Tilgjengelig fra: 2018-05-14 Laget: 2018-05-14 Sist oppdatert: 2018-05-14bibliografisk kontrollert

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