Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method

2020 ◽  
Vol 16 (9) ◽  
pp. 6103-6113 ◽  
Author(s):  
Shuiguang Deng ◽  
Zhengzhe Xiang ◽  
Peng Zhao ◽  
Javid Taheri ◽  
Honghao Gao ◽  
...  
Author(s):  
Maria V. Stepanova ◽  
◽  
Oleg I. Eremin ◽  

The article describes issues of applying an adaptive approach based on reinforcement learning for assignment of the computing tasks to nodes of distributed Internet of Things (IoT) platform. The IoT platform consists of heterogeneous elements that are computing nodes. Classical approaches, methods, and algorithms for distributed and parallel systems are not suitable for task assignment in IoT systems due to its characteristics. The reinforcement learning method allows you to solve the problem of building a distributed system due to the adaptive formation of a sequence of computational nodes and the corresponding computational tasks. Thus, the article represents a method that makes IoT nodes capable of execution computing tasks, especially, which were previously designed for classical distributed and parallel systems.


2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai

Author(s):  
Zhiyuan Xu ◽  
Jian Tang ◽  
Chengxiang Yin ◽  
Yanzhi Wang ◽  
Guoliang Xue ◽  
...  

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