期刊
IEEE INTERNET OF THINGS JOURNAL
卷 10, 期 7, 页码 6151-6163出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3222911
关键词
Resource management; Blockchains; Security; Servers; Internet of Things; Delays; Edge computing; Blockchain; deep reinforcement learning (DRL); Index Terms; Internet of Things (IoT); resource allocation
This article proposes a blockchain-enabled resource orchestration scheme for IoT using deep reinforcement learning. The scheme allows the IoT edge server and end user to reach a consensus on network resource allocation based on blockchain theory. By utilizing a policy network, the intelligent agent can perceive changes in the network's state and make dynamic resource allocation decisions. Simulation results demonstrate that the proposed scheme performs better than other security resource allocation algorithms, with average revenue, user request acceptance rate, and profitability increased by 8.5%, 1.8%, and 11.9%, respectively.
The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users' Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network's state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.
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