AI Bibliography
AI Bibliography |
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Lin, X., Li, J., Wu, J., Liang, H., & Yang, W. (2019). Making knowledge tradable in edge-ai enabled iot: A consortium blockchain-based efficient and incentive approach. IEEE Transactions on Industrial Informatics, 15(12), 6367–6378. |
| Resource type: Journal Article BibTeX citation key: Lin2019a View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science Subcategories: Big data, Command and control, Decision making, Deep learning, Doctrine, Edge AI, JADC2, Machine learning, Mosaic warfare, Q-learning Creators: Li, Liang, Lin, Wu, Yang Publisher: Collection: IEEE Transactions on Industrial Informatics |
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Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.
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