Blockchain-Enabled Intelligent Transportation Systems: A Distributed Crowdsensing Framework

Zhaolong Ning, Shouming Sun, Xiaojie Wang, Lei Guo, Song Guo, Xiping Hu, Bin Hu, Ricky Y.K. Kwok

Research output: Contribution to journalArticlepeer-review

101 Citations (Scopus)

Abstract

Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.

Original languageEnglish
Pages (from-to)4201-4217
Number of pages17
JournalIEEE Transactions on Mobile Computing
Volume21
Issue number12
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Distributed traffic management
  • blockchain
  • deep reinforcement learning
  • multi-objective optimization
  • vehicular crowdsensing

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