{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:23:10Z","timestamp":1762507390776,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,12]],"date-time":"2018-05-12T00:00:00Z","timestamp":1526083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18051532","type":"journal-article","created":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T02:57:20Z","timestamp":1526266640000},"page":"1532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5808-1382","authenticated-orcid":false,"given":"Mehrzad","family":"Lavassani","sequence":"first","affiliation":[{"name":"Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1797-1095","authenticated-orcid":false,"given":"Stefan","family":"Forsstr\u00f6m","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden"}]},{"given":"Ulf","family":"Jennehag","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden"}]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The internet of things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. 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