Resource Trading in Blockchain-Based Industrial Internet of Things

Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger fo...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-06, Vol.15 (6), p.3602-3609
Main Authors: Yao, Haipeng, Mai, Tianle, Wang, Jingjing, Ji, Zhe, Jiang, Chunxiao, Qian, Yi
Format: Article
Language:English
Subjects:
Algorithms
Blockchain
Cloud computing
cloud mining
Computer simulation
Cryptography
Distributed ledger
Economic models
Game theory
Games
Industrial applications
industrial Internet of Things (IIoT)
Informatics
Internet of Things
Machine learning
Miners
multiagent reinforcement learning
Multiagent systems
Peer-to-peer computing
Pricing
Resource management
Stackelberg game
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Summary:Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2902563