【Author】
Liu, Chang; Guo, Shaoyong; Guo, Song; Yan, Yong; Qiu, Xuesong; Zhang, Suxiang
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】With the development of smart cities, the chimney construction method can no longer meet service needs. It is extremely urgent to build a unified urban brain, and the core issue is data sharing and fusion. Aiming at the problems of data island, data leakage, and high trust cost in the IoT of the smart city, a lightweight and trusted sharing mechanism (LTSM) is proposed. First, the blockchain is combined with federated learning to realize the data sharing, which not only protects the private data, but also ensures the sharing process trust. Then, a node selection algorithm based on credit value and a node evaluation algorithm based on smart contract are designed to improve the quality of federated learning. Finally, we propose an improved raft consensus to meet the delay and security requirements of the consortium blockchain in the smart city scenario. In the simulation, we evaluate the federated learning algorithm, the node selection algorithm, and the improved raft consensus, respectively. The experimental results show that the LTSM mechanism has a good application value. The federated learning model has a better accuracy, but its training time is also longer. The node selection algorithm is helpful to improve the accuracy of the federated learning model. The improved raft consensus improves the throughput.
【Keywords】Blockchains; Collaborative work; Internet of Things; Data models; Smart cities; Security; Data privacy; Blockchain; data sharing; federated learning; smart city
【标题】LTSM：智慧城市物联网数据的轻量级可信共享机制
【摘要】随着智慧城市的发展，烟囱建造方式已经不能满足服务需求。构建统一的城市大脑迫在眉睫，核心问题是数据共享与融合。针对智慧城市物联网存在数据孤岛、数据泄露、信任成本高等问题，提出一种轻量级可信共享机制（LTSM）。首先，区块链与联邦学习相结合，实现数据共享，既保护了隐私数据，又保证了共享过程的信任。然后，设计了基于信用值的节点选择算法和基于智能合约的节点评估算法，以提高联邦学习的质量。最后，我们提出了一种改进的 raft 共识，以满足智慧城市场景中联盟区块链的时延和安全要求。在模拟中，我们分别评估了联邦学习算法、节点选择算法和改进的 raft 共识。实验结果表明，LTSM机制具有良好的应用价值。联邦学习模型具有更好的准确性，但它的训练时间也更长。节点选择算法有助于提高联邦学习模型的准确性。改进的 raft 共识提高了吞吐量。
【关键词】区块链；协作工作；物联网;数据模型；智慧城市；安全;数据隐私；区块链；数据共享；联邦学习；智慧城市
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