| YANG Zhaoxin (杨兆鑫)*,YANG Ruizhe*,LI Meng*,YU Richard Fei* **,ZHANG Yanhua*.[J].高技术通讯(英文),2022,28(1):10~20 |
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| A load balance optimization framework for sharded-blockchain enabled Internet of Things |
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| DOI：10.3772/j.issn.1006-6748.2022.01.002 |
| 中文关键词: |
| 英文关键词: Internet of Things (IoT), blockchain, sharding, load balance, deep reinforcement learning (DRL) |
| 基金项目: |
| Author Name | Affiliation | | YANG Zhaoxin (杨兆鑫)* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**School of Information Technology, Carleton University, Ottawa K1S 5B6, Canada) | | YANG Ruizhe* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**School of Information Technology, Carleton University, Ottawa K1S 5B6, Canada) | | LI Meng* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**School of Information Technology, Carleton University, Ottawa K1S 5B6, Canada) | | YU Richard Fei* ** | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**School of Information Technology, Carleton University, Ottawa K1S 5B6, Canada) | | ZHANG Yanhua* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**School of Information Technology, Carleton University, Ottawa K1S 5B6, Canada) |
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| 中文摘要: |
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| 英文摘要: |
| Recently, sharded-blockchain has attracted more and more attention. Its inherited immutability, decentralization, and promoted scalability effectively address the trust issue of the data sharing in the Internet of Things (IoT). Nevertheless, the traditional random allocation between validator groups and transaction pools ignores the differences of shards, which reduces the overall system performance due to the unbalance between computing capacity and transaction load. To solve this problem, a load balance optimization framework for sharded-blockchain enabled IoT is proposed, where the allocation between the validator groups and transaction pools is implemented reasonably by deep reinforcement learning (DRL). Specifically, based on the theoretical analysis of the intra-shard consensus and the final system consensus, the optimization of system performance is formed as a Markov decision process (MDP), and the allocation of the transaction pools, the block size, and the block interval are jointly trained in the DRL agent. The simulation results show that the proposed scheme improves the scalability of the sharded blockchain system for IoT. |
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