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.

Abstract:The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist of various multimedia contents, such as live audio and video. This article presents a solution for the communication of high definition video in low-delay scenarios (<200 ms) under the constraints of devices with limited hardware resources, such as the Raspberry Pi. We verify that it is possible to enable low delay video streaming between Raspberry Pi devices using a distributed Internet of Things system called the SensibleThings platform. Specifically, our implementation transfers a 6 Mbps H.264 video stream of 1280 × 720 pixels at 25 frames per second between devices with a total delay of 181 ms on the public Internet, of which the overhead of the distributed Internet of Things communication platform only accounts for 18 ms of this delay. We have found that the most significant bottleneck of video transfer on limited Internet of Things devices is the video coding and not the distributed communication platform, since the video coding accounts for 90% of the total delay.

We see a shift from todays Internet-of-Things (IoT) 1 to include more industrial equipment and metrology systems, 2 forming the Industrial Internet of Things (IIoT). However, this 3 leads to many concerns related to confidentiality, integrity, 4 availability, privacy and non-repudiation. Hence, there is a need 5 to secure the IIoT in order to cater for a future with smart grids, 6 smart metering, smart factories, smart cities, and smart manu-7 facturing. It is therefore important to research IIoT technologies 8 and to create order in this chaos, especially when it comes to 9 securing communication, resilient wireless networks, protecting 10 industrial data, and safely storing industrial intellectual property 11 in cloud systems. This research therefore presents the challenges, 12 needs, and requirements of industrial applications when it comes 13 to securing IIoT systems.14

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