Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach

【Author】 Li, Meng; Pei, Pan; Yu, F. Richard; Si, Pengbo; Li, Yu; Sun, Enchang; Zhang, Yanhua

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.

【Keywords】Internet of Things; Blockchains; 6G mobile communication; Resource management; Optimization; Computational modeling; Servers; Blockchain; collective reinforcement learning (CRL); Internet of Things (IoT); mobile-edge computing (MEC); sixth generation (6G)

【发表时间】2022 15-Nov

【收录时间】2022-11-20

【文献类型】实证数据

【主题类别】

区块链技术-协同技术-物联网