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Sensors 2018, 18(5), 1532; https://doi.org/10.3390/s18051532

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT

Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden
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Received: 10 March 2018 / Revised: 27 April 2018 / Accepted: 9 May 2018 / Published: 12 May 2018
(This article belongs to the Special Issue Dependable Monitoring in Wireless Sensor Networks)
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Abstract

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. View Full-Text
Keywords: data mining; fog computing; IoT; online learning; monitoring data mining; fog computing; IoT; online learning; monitoring
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Lavassani, M.; Forsström, S.; Jennehag, U.; Zhang, T. Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT. Sensors 2018, 18, 1532.

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