Porting Rulex Machine Learning Software to the Raspberry Pi as an Edge Computing Device.

With the rise of Internet of Things (IoT) and Edge Computing, which are technologies that rely on smart and low power computing nodes with adequate processing power and storage capabilities, it is expected that Artificial Intelligence and machine learning will play a role in the continuous spreading of their application fields.One of the most adopted hardware platforms for IoT and Machine Learning is the low-cost, multipurpose Raspberry Pi, which is small enough and still capable of effectively handling machine learning tasks. Moreover, it is ideal for development and educational purposes. On the other hand, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex [] as a good candidate as an ML engine, suitable for advanced edge computing applications.In this paper, we report the deployment of the machine learning package Rulex to operate on the Raspberry Pi in multiple arrangements. The target is to perform training and testing of Machine Learning algorithms through running Rulex on the Raspberry PI as an Edge Computing Device.Specifically, we describe the process of porting Rulex external and internal libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Moreover, we present the standalone and Client/Server Configuration of Rulex on the Raspberry Pi along with the Remote Development configuration used to compile and debug the Rulex source code remotely. We have applied Forecasts using training and testing data sets on the Raspberry Pi as an IoT Device, which generate promising and accurate results.