Machine Learning to Support Self-Configuration of Industrial Systems Interconnected over Wi-Fi

Communication quality of wireless links unavoidably varies over time, for a number of reasons that are mostly unknown in the design phase of distributed systems. This is a severe limitation when such network technologies are employed to connect devices and subsystems in industrial applications, where high reliability and timeliness are customarily demanded. The ability to foresee, to some extent, the quality of a link in the near future is likely to provide clear benefits in many such cases.In this paper, we analyzed and compared the performance of two approaches, based on simple moving averages and artificial neural networks, respectively, to carry out predictions for a real Wi-Fi link. Results confirm that reliable estimates about the packet delivery ratio can be obtained in realistic operating conditions by averaging the recent past, and that the use of machine learning may improve prediction further.