TY - JOUR
T1 - A Secure Mobile Crowdsensing Game with Deep Reinforcement Learning
AU - Xiao, Liang
AU - Li, Yanda
AU - Han, Guoan
AU - Dai, Huaiyu
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received March 2, 2017; revised June 4, 2017 and July 12, 2017; accepted July 13, 2017. Date of publication August 9, 2017; date of current version November 20, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 61671396, in part by the U.S. National Science Foundation under Grant ECCS-1307949, Grant EARS-1444009, and Grant CMMI-1435778, and in part by the U.S. Army Research Office under Grant W911NF-17-1-0087 and Grant W911NF-16-1-0448. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Guofei Gu. (Corresponding author: Liang Xiao.) L. Xiao is with the School of Data and Computer Science, Sun Yat-sen University, Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China (e-mail: xiaoliang3@mail.sysu.edu.cn).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - Mobile crowdsensing
KW - deep Q-networks
KW - deep reinforcement learning
KW - faked sensing attacks
KW - game theory
UR - http://www.scopus.com/inward/record.url?scp=85028994901&partnerID=8YFLogxK
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U2 - 10.1109/TIFS.2017.2737968
DO - 10.1109/TIFS.2017.2737968
M3 - Article
AN - SCOPUS:85028994901
SN - 1556-6013
VL - 13
SP - 35
EP - 47
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
IS - 1
M1 - 8006228
ER -