LTSM: Lightweight and Trusted Sharing Mechanism of IoT Data in Smart City

Chang Liu, Shaoyong Guo, Song Guo, Yong Yan, Xuesong Qiu, Suxiang Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Pages (from-to)5080-5093
Number of pages14
JournalIEEE Internet of Things Journal
Volume9
Issue number7
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • Blockchain
  • Data sharing
  • Federated learning
  • Smart city

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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