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.
Highlights
A current global trend is the digitalization of all aspects of our world
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
The first question involved investigating to what extent we can save energy and reduce the number of packet transmissions from sensor devices by introducing a data stream model learned at the wireless sensor networks (WSNs) layer and approximated at the fog layer
Summary
An example is applications that can utilize information from sensors attached to things in order to provide automatized operation and imitate intelligent behavior This concept is commonly referred to as the Internet-of-Things (IoT) [1], and its application areas have spread to include industrial applications [2]. Most IoT solutions today are based on traditional client-server architectures with cloud back-ends, attached data analysis, visualization, and end user data access on the cloud side. This approach does, have some limitations, such as the cloud servers becoming single points of failure and an excessive delay when sending data to a cloud server far away on the Internet. When the fog servers act as cloud systems closer to the edges of the network, it can offload computationally heavy tasks of the IoT devices
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