Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system

The monitoring of bird populations provides valuable insights into biodiversity variations and their correlation with environmental changes. This study proposes a flexible hybrid edge computing IoT architecture for a low-cost bird song detection system. The system integrates low-power microcomputers, such as Raspberry Pi, equipped with USB microphones, LoRa modules, and Wi-Fi for seamless operation across rural and urban environments. By utilizing deep learning techniques, including convolutional neural networks (CNNs) trained on bird song datasets, the system performs real-time species detection at the edge, minimizing the need for high-bandwidth transmission. Nodes dynamically select communication technologies based on availability, sending data to an IoT analytics platform. Field deployments demonstrate the system's efficiency, interoperability, and adaptability for biodiversity monitoring, particularly in remote areas with limited connectivity. This architecture addresses the challenges of real-time species detection while ensuring low cost, scalability, and energy efficiency. The main advantage is that devices can operate in areas without mobile coverage, as they only transmit the detection signal. This results in significant bandwidth savings, since the processing is carried out at the edge.