Journal
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume 9, Issue 1, Pages 159-173Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2021.3100258
Keywords
Blockchains; Collaborative work; Edge computing; Task analysis; Computational modeling; Reliability; Data privacy; Blockchain; edge computing; federated learning; marine Internet of things (MIoT); privacy
Funding
- National Natural Science Foundation of China [61902052]
- National Key Research and Development Plan [2017YFC0821003-2]
- Science and Technology Major Industrial Project of Liaoning Province [2020JH1/10100013]
- Dalian Science and Technology Innovation Fund [2019J11CY004, 2020JJ26GX037]
- Fundamental Research Funds for the Central Universities [DUT20ZD210, DUT20TD107]
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This article proposes a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology, ensuring privacy of nodes and storing federated learning workers for non-tampering security. The concept of quality and reputation are used as selection metrics for federated learning workers, with a quality proof mechanism (PoQ) designed and applied to the blockchain to enhance the quality of edge nodes recorded.
The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.
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