期刊
IEEE WIRELESS COMMUNICATIONS
卷 29, 期 3, 页码 32-38出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MWC.004.2100616
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资金
- National Key Research and Development Plan [2021YFB2700302]
- National Natural Science Foundation of China [62172453, 62032025, 62172438]
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X355]
- Pearl River Talent Recruitment Program [2019QN01X130]
- Science and Technology Research Program of Chongqing Municipal Education Commission [KJZD-K201802401]
This study proposes a real-time and reliable sharing framework with fine-grained transaction support based on blockchain technology, using a two-layer scaling blockchain design. In the on-chain layer, sharing-oriented sharding is employed for secure and efficient processing of macro-transactions. In the off-chain layer, cross-zone off-chain channels are set up to enable real-time sharing transactions with high-frequency micro-trading scenarios.
In the future sharing economy, billions of underutilized IoT devices will be deployed to enable a powerful and large-scale sharing market that produces economic, environmental, and social benefits. Given the fact that communications in numerous IoT devices through wireless links are unreliable, blockchain technology, as a promising solution, has emerged to achieve reliable and secure sharing services in a decentralized manner. However, applying blockchain in large-scale wireless networks confronts scalability challenges. This motivates us to propose a real-time, trusted data interactive, and fine-grained transaction supportable sharing framework, the core of which is a novel two-layer scaling blockchain design. In the on-chain layer, sharing-oriented sharding is employed to enable secure and efficient processing of macro-transactions on the chain. In the off-chain layer, cross-zone off-chain channels are set up to provide real-time sharing transactions with high-frequency micro-trading scenarios. Finally, a proof-of-concept case study of electric vehicle sharing data is implemented with experimental results to demonstrate the feasibility of our framework.
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