AI-Enhanced Resource Allocation for LPWAN-Based LoRaWAN:A Hybrid TinyML and Deep Learning Approach

초록

The integration of artificial intelligence (AI) with low power wide area networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices (EDs) presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid tiny machine learning (TinyML) and deep neural network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D convolutional neural network (CNN) and long short-term memory (LSTM) model implemented on the network server, combined with TinyML models deployed on EDs. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable EDs to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments.

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