Cloud–Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach
Cloud–Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach
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支持区块链的物联网的云边缘协作资源分配：集体强化学习方法
DOI：
10.1109/jiot.2022.3185289
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发表时间：
2022-11
影响因子：
10.6
通讯作者：
Yanhua Zhang
中科院分区：
文献类型：
--
作者：
Meng Li;Pan Pei;F. Richard Yu;Pengbo Si;Yu Li;Enchang Sun;Yanhua Zhang
在众多新兴移动的设备和各种服务质量（QoS）需求的驱动下，移动边缘计算（MEC）已被认为是提升移动的设备的计算能力以及减少物联网（IoT）应用的能量开销和服务延迟的有前景的范例。然而，现有的研究工作还存在一些问题：1）网络和计算资源有限; 2）资源管理简单或不智能; 3）安全性和可靠性被忽视。为了科普这些问题，本文考虑使用6 G和区块链技术来提高网络性能，并确保MEC物联网数据共享的真实性。同时，提出并介绍了一种新的智能优化方法--集体强化学习（CRL），实现了资源的智能分配，满足了分布式训练结果的共享，避免了系统资源的过度消耗。基于所设计的网络模型，制定了云边缘协同资源分配框架。通过联合优化卸载决策、块间隔和发送功率，以最小化系统能量和延迟的消耗开销。然后将问题设计成一个马尔可夫决策过程，通过CRL得到最优策略。仿真结果表明，基于该方案的系统性能明显优于现有方案。
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.
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影响因子：
10.6
作者：
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DOI：
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影响因子：
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影响因子：
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