An evaluation of how edge computing is enabling the opportunities for Industry 4.0

Connecting factories to the internet and enable the possibilities for these to autonomously talk to each other is called the Industrial Internet of Things(IIoT) and is mentioned as Industry 4.0 in the terms of the industrial revolutions. The machines are collecting data through very many different sensors and need to share these values with each other and the cloud. This will make a large load to the cloud and the internet, and the latency will be large. To evaluate how the workload and the latency can be reduced and still get the same result as using the cloud, two different systems are implemented. One which uses cloud and one which using edge computing. Edge computing is when the processing of the data is decentralized to the edge of the network. This thesis aims to find out ”When is it more favorable to use an edge solution and when is it to prefer a cloud solution”. The first system is implemented with an edge platform, Crosser, the second system is implemented with a cloud platform, Azure. Both implementations are giving the same outputs but the differences is where the data is processed. The systems are measured in latency, bandwidth, and CPU usage. The result of the measurements shows that the Crosser system has less latency, using smaller bandwidth but is needing more computational power of the device which is on the edge of the network. The conclusion of the results is that it depends on the demands of the system. Is the demands that it should have low latency and not using much bandwidth Crosser is to prefer. But if a very heavy machine learning algorithm is going to be executed in the system and the latency and bandwidth size is not a problem then the Cloud Reference System is to prefer