.png){kind=icon}
返回
AI-Driven Collaborative Resource Allocation for Task Execution in 6G-Enabled Massive IoT
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2021.3051031.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.
Keyword:
Resource management
Task analysis
6G mobile communication
Dynamic scheduling
Heuristic algorithms
Vehicle dynamics
Real-time systems
Artificial intelligence
edge computing
Internet of Things (IoT)
resource allocation
sixth-generation mobile networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Joint Multiuser DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence面向协作边缘智能的联合多用户DNN划分和计算资源分配
OAAI
OAAI分享收藏IoT Resource Allocation and Optimization Based on Heuristic Algorithm基于启发式算法的物联网资源分配与优化.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach移动边缘计算的智能资源分配: 一种深度强化学习方法
PREAI
分享收藏In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated LearningIn-Edge AI: 通过联合学习实现移动边缘计算、缓存和通信的智能化IEEE NETWORKOAAI
IEEE NETWORKIF6.3
OAAI分享收藏Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method可信物联网系统Edge中的动态资源分配: 一种强化学习方法
PREAI
分享收藏分享收藏
查看更多内容