A Two-Board Design for Interconnected Intelligence in Tiny IoT

Machine learning is nowadays used in tiny micro-controller boards, sometimes called TinyML. At the same time, LoRa Internet of Things (IoT) communication technology can now provide LoRa mesh network topologies that interconnect remote IoT devices. Both tendencies, however, have not converged yet to combine the potential of both. In this paper, we argue for a solution consisting of a two-board design as an architecture for interconnected ML-enabled IoT nodes in a LoRa mesh network. A prototype of the architecture using the TTGO LoRa32 board as a network device and the Arduino Portenta H7 as an application device is implemented as a case study to show the feasibility. We analyze a federated learning protocol design to illustrate how distributed applications can interact within the proposed two-board node architecture. We position the architecture with regard to several performance aspects and observe that the design delivers a separation of concerns by one board hosting the ML-application and the other providing an advanced network service to the application.