A Secure Mobile Crowdsensing Game with Deep Reinforcement Learning

Liang Xiao, Yanda Li, Guoan Han, Huaiyu Dai, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

138 Scopus citations


Mobile crowdsensing (MCS) is vulnerable to faked sensing attacks, as selfish smartphone users sometimes provide faked sensing results to the MCS server to save their sensing costs and avoid privacy leakage. In this paper, the interactions between an MCS server and a number of smartphone users are formulated as a Stackelberg game, in which the server as the leader first determines and broadcasts its payment policy for each sensing accuracy. Each user as a follower chooses the sensing effort and thus the sensing accuracy afterward to receive the payment based on the payment policy and the sensing accuracy estimated by the server. The Stackelberg equilibria of the secure MCS game are presented, disclosing conditions to motivate accurate sensing. Without knowing the smartphone sensing models in a dynamic version of the MCS game, an MCS system can apply deep Q-network (DQN), which is a deep reinforcement learning technique combining reinforcement learning and deep learning techniques, to derive the optimal MCS policy against faked sensing attacks. The DQN-based MCS system uses a deep convolutional neural network to accelerate the learning process with a high-dimensional state space and action set, and thus improve the MCS performance against selfish users. Simulation results show that the proposed MCS system stimulates high-quality sensing services and suppresses faked sensing attacks, compared with a Q-learning-based MCS system.

Original languageEnglish (US)
Article number8006228
Pages (from-to)35-47
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Issue number1
StatePublished - Jan 2018

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


  • Mobile crowdsensing
  • deep Q-networks
  • deep reinforcement learning
  • faked sensing attacks
  • game theory


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