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Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Network (CSN))ORCID iD: 0000-0001-5808-1382
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Network (CSN))ORCID iD: 0000-0002-1797-1095
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Network (CSN))
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Network (CSN))
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1532Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
MDPI , 2018. Vol. 18, no 5, article id 1532
Keywords [en]
data mining, fog computing, IoT, online learning, monitoring
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:miun:diva-33609DOI: 10.3390/s18051532ISI: 000435580300231PubMedID: 29757227Scopus ID: 2-s2.0-85047063861OAI: oai:DiVA.org:miun-33609DiVA, id: diva2:1205328
Funder
Knowledge Foundation, 20150363, 20140321, and 20140319Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-09-26Bibliographically approved

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Publisher's full textPubMedScopushttp://www.mdpi.com/1424-8220/18/5/1532

Authority records BETA

Lavassani, MehrzadForsström, StefanJennehag, UlfZhang, Tingting

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CiteExportLink to record
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