Edge AI and ML Algorithms for Energy-Efficient IoT Applications
Abstract
The proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generation at the network's edge. To alleviate the burden on centralized cloud infrastructure and address latency concerns, Edge Computing has emerged as a promising paradigm. Concurrently, the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms at the edge has presented unprecedented opportunities for real-time data analysis and decision-making in IoT applications.
This research paper investigates the utilization of Edge AI and ML algorithms specifically tailored for enhancing energy efficiency in IoT deployments. It explores novel approaches to optimize resource-constrained edge devices while ensuring high computational performance. The study focuses on designing lightweight AI models, tailored ML algorithms, and intelligent data processing techniques that minimize energy consumption without compromising the accuracy and reliability of IoT applications.
Moreover, the paper examines various case studies and experiments conducted in diverse IoT scenarios, illustrating the practical implementation and effectiveness of Edge AI and ML algorithms for energy-efficient IoT applications. The findings shed light on the potential benefits, challenges, and future directions in leveraging these technologies to achieve sustainable and energy-conscious IoT ecosystems at the network's edge.
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