Blockchain-enabled Intelligent Transportation Systems: A Distributed Crowdsensing Framework

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

48 Citations (Scopus)


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)1-17
JournalIEEE Transactions on Mobile Computing
Publication statusAccepted/In press - 2021


  • Blockchain
  • blockchain
  • Crowdsensing
  • deep reinforcement learning
  • Distributed traffic management
  • multi-objective optimization
  • Optimization
  • Roads
  • Safety
  • Servers
  • Task analysis
  • vehicular crowdsensing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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