While Industrial Edge AI applications are deployed at different devices, such as local clusters of computers, gateways, IoT devices and fog nodes, Embedded AI focuses on the deployment of AI algorithms in embedded units with limited computing resources, such as microcontrollers (MCUs). The increase of computational power, the decrease of the cost of the hardware, and the development of less computation intensive AI models is fostering new opportunities to integrate embedded devices into Industrial Edge AI applications. This paper proposes an MCU based architecture and network for a low-cost monitoring of non-critical industrial environments. These MCUs both (1) monitor and perform a preliminary analysis of collected data with embedded AI, and (2) send data to edge devices with more computational power executing AI algorithms. The paper proposes an architecture for this MCU based network to lay the foundation of extensive industrial environmental monitoring where AI algorithms could for example, enhance workers safety, optimize processes, and improve maintenance operations. The technological viability of the network has been validated with a testbed where different MCUs (based on Arduino and ESP32) capture temperature and humidity data and send it to a more powerful edge device. Successful results prove the viability of the approach and foster further validations within real industrial scenarios.