Distributed Adaptive Coding Optimization for IoT Using Fulcrum Code and Model-Agnostic Meta-Learning (MAML) in Ultra-Low Latency Environments

Distributed Adaptive Coding Optimization for IoT Using Fulcrum Code and Model-Agnostic Meta-Learning (MAML) in Ultra-Low Latency Environments

The exponential growth of the Internet of Things (IoT) has amplified the demand for energy-conscious, low-latency, and efficient data transmission techniques. This study proposes an adaptive distributed coding scheme combining Fulcrum Code and Model-Agnostic Meta-Learning (MAML) to optimize IoT comm...

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Bibliographic Details
Main Authors: Yair Rivera Julio, Angel Pinto, Rodrigo Garcia, Jose Aguilar, Nelson A. Perez-Garcia
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Fulcrum code
IoT
meta-learning
MAML
adaptive coding
ultra-low latency
Online Access:https://ieeexplore.ieee.org/document/11003082/
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