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Number of IoT devices is constantly increasing which results in greatercomplexity of computations and high data velocity. One of the approach toprocess sensor data is dataflow programming. It enables the development ofreactive software with short processing and rapid response times, especiallywhen moved to the edge of the network. This is especially important in systemsthat utilize online machine learning algorithms to analyze ongoing processessuch as those observed in Industry 4.0. In this paper, we show thatorganization of such systems depends on the entire processing stack, from thehardware layer all the way to the software layer, as well as on the requiredresponse times of the IoT system. We propose a flow processing stack for suchsystems along with the organizational machine learning architectural patternsthat enable the possibility to spread the learning and inferencing on the edgeand the cloud. In the paper, we analyse what latency is introduced bycommunication technologies used in the IoT for cloud connectivity and how theyinfluence the response times of the system. Finally, we are providingrecommendations which machine learning patterns should be used in the IoTsystems depending on the application type.

Machine Learning in the Internet of Things for Industry 4.0