LoRaWAN Low Power Wide Area Networks have become a standard for remote sensing over large geographical areas with limited access to energy; however, this low-power capability inherently limits communication throughput, posing scalability challenges as network density increases. Edge intelligence (edge AI) can reduce network traffic by deploying lightweight models locally that classify and filter local data before transmission to remote servers. However, models trained with limited local data that run on devices with restricted memory and execution capability necessitate continuous updates to remain accurate within dynamic environments. Edge intelligence methods, such as federated learning, transfer global model updates to end devices for incremental training and local adaptation. While model transmission is possible using Firmware Update Over The Air (FUOTA) in LoRaWAN, current implementations necessitate manual selection of transmission parameters that highly impact update duration and energy efficiency. This paper proposes a novel approach for updating models over LoRaWAN, addressing distinct and significant challenges compared to typical scenarios over Wi-Fi or cellular networks. First, we fully automate the FUOTA process to propagate model updates to selected machines with heterogeneous communication capabilities. Then, we propose three parameter selection policies to balance the energy consumed by devices and the time taken to update entire networks. An evaluation on a testbed demonstrates the effectiveness of our approach in propagating updates automatically. Large-scale simulations of up to 300 end devices show that our energy-oriented update policy reduces the energy consumption of end devices between 2.2x and 2.7x compared to state-of-the-art baselines.

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