Computer Science ›› 2020, Vol. 47 ›› Issue (10): 41-47.doi: 10.11896/jsjkx.200700070

Special Issue: Mobile Crowd Sensing and Computing

• Mobile Crowd Sensing and Computing • Previous Articles     Next Articles

Reinforcement Learning Based Win-Win Game for Mobile Crowdsensing

CAI Wei, BAI Guang-wei, SHEN Hang, CHENG Zhao-wei, ZHANG Hui-li   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Received:2020-07-12 Revised:2020-08-01 Online:2020-10-15 Published:2020-10-16
  • About author:CAI Wei,born in 1997,postgraduate.His main research interests include privacy protection,mobile crowdsensing and reinforcement learning.
    SHEN Hang,born in 1984,Ph.D,asso-ciate professor,master supervisor,is a member of China Computer Federation.His main research interests include cyber security,privacy protection and 5G network.
  • Supported by:
    National Natural Science Foundation of China (61502230),Natural Science Foundation of Jiangsu Province (BK20150960),Jiangsu Province “Six Talent Peaks” High-level Talent Project (RJFW-020) and State Key Laboratory of New Technology of Computer Software (Nanjing University) Project (KFKT2017B21)

Abstract: Mobile crowdsensing system should offer the personalized privacy protection of users’ location to attract more users to participate in the task.However,due to the existence of malicious attackers,users’ enhanced privacy protection will lead to poor location availability and reduce the efficiency of task allocation.To solve this problem,this paper proposes a win-win game based on reinforcement learning.Firstly,two virtual entities of the trusted third party are used to simulate the interaction between users and the platform,one simulating user chooses the privacy budget to add noise to their locations and the other simulates the platform allocating tasks with users’ disturbed locations.Then,the interaction process is constructed as a game,in which the two virtual entities of interaction are the adversaries,and the equilibrium point is derived.Finally,the reinforcement learning method is used to try different location disturbance strategies and output an optimal location disturbance scheme.The experimental results show that the mechanism can optimize the task distribution utility while improving the user’s overall utility as much as possible,so that the user and the platform can achieve a win-win situation.

Key words: Game theory, Mobile crowdsensing, Personalized privacy-preserving, Reinforcement learning, Task allocation

CLC Number: 

  • TP393
[1]WANG L Y,ZHANG D Q,WANG Y S,et al.Sparse MobileCrowdsensing:Challenges and Opportunities[J].IEEE Communications Magazine,2016,54(7):161-167.
[2]TANG Y,LIU R Q,YANG P L,et al.A Secure Task Allocation Technology Based on Crowd Sensing Network [J].Computer Engineering,2016,42(6):161-166.
[3]GUO B,LIU Y,WU W L,et al.ActiveCrowd:A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems[J].IEEE Transactions on Human-Machine Systems,2017,47(3):392-403.
[4]LIU Y,GUO B,WANG Y,et al.TaskMe:Multi-Task Allocation in Mobile Crowd Sensing [C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.2016:403-414.
[5]WANG L Y,ZHANG D Q,PATHAK A,et al.CCS-TA:Quality-Guaranteed Online Task Allocation in Compressive Crowdsensing[C]//Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing.2015:683-694.
[6]QIAN Y F,JIANG Y Y,HOSSAIN M S,et al.Privacy-Preserving based Task Allocation with Mobile Edge Clouds[J].Information Sciences,2020,507:288-297.
[7]LIU B,ZHOU W L,ZHU T Q,et al.Invisible Hand:A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models[J].IEEE Transactions on Vehicular Technology,2016,66(5):4410-4423.
[8]TO H,GHINITA G,SHAHABI C.A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing[J].Proceedings of the VLDB Endowment,2014,7(10):919-930.
[9]POURNAJAF L,XIONG L,SUNDERAM V,et al.Spatial Task Assignment for Crowd Sensing with Cloaked Locations[C]//2014 IEEE 15th International Conference on Mobile Data Ma-nagement.IEEE,2014,1:73-82.
[10]WANG T C,LIU Y,JIN X,et al.Research on K-Anonymity-Based Privacy Protection in Crowd Sensing[J].Journal on Communications,2018,39(A01):170-178.
[11]LONG H,ZHANG S K,ZHANG L.Privacy Protection Method Based on Voronoi Cell in Crowd Sensing[J].Computer Engineering,2020,46(5):181-186,192.
[12]DWORK C.Differential Privacy:A Survey of Results[C]//International Conference on Theory and Applications of Models of Computation.Springer,Berlin,Heidelberg,2008:1-19.
[13]XIONG J B,MA R,CHEN L,et al.A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT[J].IEEE Transactions on Industrial Informatics,2020,16(6):4231-4241.
[14]WANG L Y,YANG D Q,HAN X,et al.Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation[C]//Proceedings of the 26th International Conference on World Wide Web.2017:627-636.
[15]WANG Z B,HU J H,LV R Z,et al.Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing[J].IEEE Transactions on Mobile Computing,2019,18(6):1330-1341.
[16]NIE J T,LUO J,XIONG Z H,et al.A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing[J].IEEE Transactions on Wireless Communications,2019,18(1):724-738.
[17]XIAO L,CHEN T H,XIE C X,et al.Mobile Crowdsensing Games in Vehicular Networks[J].IEEE Transactions on Vehi-cular Technology,2017,67(2):1535-1545.
[18]ALSHEIKH M A,NIYATO D,LEONG D,et al.Privacy Mana-gement and Optimal Pricing in People-Centric Sensing[J].IEEE Journal on Selected Areas in Communications,2017,35(4):906-920.
[19]CHATZIKOKOLAKIS K,ANDRÉS M E,BORDENABE N E,et al.Broadening the Scope of Differential Privacy Using Metrics[C]//International Symposium on Privacy Enhancing Technologies Symposium.Springer,Berlin,Heidelberg,2013:82-102.
[1] LIU Xing-guang, ZHOU Li, LIU Yan, ZHANG Xiao-ying, TAN Xiang, WEI Ji-bo. Construction and Distribution Method of REM Based on Edge Intelligence [J]. Computer Science, 2022, 49(9): 236-241.
[2] JIANG Yang-yang, SONG Li-hua, XING Chang-you, ZHANG Guo-min, ZENG Qing-wei. Belief Driven Attack and Defense Policy Optimization Mechanism in Honeypot Game [J]. Computer Science, 2022, 49(9): 333-339.
[3] YUAN Wei-lin, LUO Jun-ren, LU Li-na, CHEN Jia-xing, ZHANG Wan-peng, CHEN Jing. Methods in Adversarial Intelligent Game:A Holistic Comparative Analysis from Perspective of Game Theory and Reinforcement Learning [J]. Computer Science, 2022, 49(8): 191-204.
[4] SHI Dian-xi, ZHAO Chen-ran, ZHANG Yao-wen, YANG Shao-wu, ZHANG Yong-jun. Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning [J]. Computer Science, 2022, 49(8): 247-256.
[5] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[6] LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279.
[7] FANG Tao, YANG Yang, CHEN Jia-xin. Optimization of Offloading Decisions in D2D-assisted MEC Networks [J]. Computer Science, 2022, 49(6A): 601-605.
[8] XIE Wan-cheng, LI Bin, DAI Yue-yue. PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing [J]. Computer Science, 2022, 49(6): 3-11.
[9] XU Hao, CAO Gui-jun, YAN Lu, LI Ke, WANG Zhen-hong. Wireless Resource Allocation Algorithm with High Reliability and Low Delay for Railway Container [J]. Computer Science, 2022, 49(6): 39-43.
[10] HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong. Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration [J]. Computer Science, 2022, 49(6): 149-157.
[11] GUO Yu-xin, CHEN Xiu-hong. Automatic Summarization Model Combining BERT Word Embedding Representation and Topic Information Enhancement [J]. Computer Science, 2022, 49(6): 313-318.
[12] FAN Jing-yu, LIU Quan. Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [J]. Computer Science, 2022, 49(6): 335-341.
[13] ZHANG Jia-neng, LI Hui, WU Hao-lin, WANG Zhuang. Exploration and Exploitation Balanced Experience Replay [J]. Computer Science, 2022, 49(5): 179-185.
[14] LI Peng, YI Xiu-wen, QI De-kang, DUAN Zhe-wen, LI Tian-rui. Heating Strategy Optimization Method Based on Deep Learning [J]. Computer Science, 2022, 49(4): 263-268.
[15] TAN Zhen-qiong, JIANG Wen-Jun, YUM Yen-na-cherry, ZHANG Ji, YUM Peter-tak-shing, LI Xiao-hong. Personalized Learning Task Assignment Based on Bipartite Graph [J]. Computer Science, 2022, 49(4): 269-281.
Full text



No Suggested Reading articles found!