Enhancing Edge AI Performance for Real-Time IoT Applications
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Abstract
The rapid growth of the Internet of Things (IoT) has led to an increased demand for real-time processing capabilities, making edge computing and AI integral to many IoT systems. However, the performance of Edge AI (Artificial Intelligence) systems for real-time IoT applications faces challenges such as limited computational resources, latency, and energy efficiency. This paper proposes methods to enhance the performance of Edge AI systems in real-time IoT contexts by optimizing AI models, utilizing efficient edge computing architectures, and addressing resource constraints. Through comparative experiments, we analyze the trade-offs between model accuracy, computational overhead, and system latency. Results indicate that leveraging lightweight models and optimizing data processing pipelines can significantly improve system performance. This work contributes to the development of efficient, scalable AI systems for IoT applications, with practical implications for smart cities, autonomous vehicles, and industrial automation
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