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Loading...Introduction to Edge AI on LoRaWAN Networks
Last quarter, our team discovered the importance of edge AI in industrial IoT applications, particularly on LoRaWAN networks. We were working on a project that required real-time data processing and analytics on devices with limited computational resources. After trying out various approaches, we settled on using TensorFlow Lite Micro and TinyML on STM32 microcontrollers. Here's what I learned when comparing these two frameworks for edge AI applications.
Background on LoRaWAN and Edge AI
LoRaWAN is a wireless communication protocol designed for long-range, low-power, and low-bandwidth IoT applications. It's widely used in industrial settings due to its reliability, security, and low cost. Edge AI, on the other hand, refers to the practice of processing data closer to the source, reducing latency, and improving real-time decision-making. In our case, we needed to deploy AI models on edge devices that could collect and analyze data from sensors in real-time.
TensorFlow Lite Micro
TensorFlow Lite Micro is a lightweight version of the popular TensorFlow framework, optimized for microcontrollers and other resource-constrained devices.
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