TY - JOUR
T1 - Multiagent Actor-Critic Network-Based Incentive Mechanism for Mobile Crowdsensing in Industrial Systems
AU - Gu, Bo
AU - Yang, Xinxin
AU - Lin, Ziqi
AU - Hu, Weiwei
AU - Alazab, Mamoun
AU - Kharel, Rupak
N1 - Funding Information:
Manuscript received April 29, 2020; revised July 4, 2020, August 15, 2020, and August 28, 2020; accepted September 5, 2020. Date of publication September 21, 2020; date of current version June 16, 2021. This work was supported by the National Key Research and Development Program of China under Grant 2019YFB1704702. Paper no. TII-20-2140. (Corresponding author: Bo Gu.) Bo Gu, Xinxin Yang, Ziqi Lin, and Weiwei Hu are with the School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China (e-mail: [email protected]; [email protected]; [email protected]; Huww1998@ 163.com).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Mobile crowdsensing (MCS) is an appealing sensing paradigm that leverages the sensing capabilities of smart devices and the inherent mobility of device owners to accomplish sensing tasks with the aim of constructing powerful industrial systems. Incentivizing mobile users (MUs) to participate in sensing activities and contribute high-quality data is of paramount importance to the success of MCS services. In this article, we formulate the competitive interactions between a sensing platform (SP) and MUs as a multistage Stackelberg game with the SP as the leader player and the MUs as the followers. Given the unit prices announced by MUs, the SP calculates the quantity of sensing time to purchase from each MU by solving a convex optimization problem. Then, each follower observes the trading records and iteratively adjusts their pricing strategy in a trial-and-error manner based on a multiagent deep reinforcement learning algorithm. Simulation results demonstrate the efficiency of the proposed method.
AB - Mobile crowdsensing (MCS) is an appealing sensing paradigm that leverages the sensing capabilities of smart devices and the inherent mobility of device owners to accomplish sensing tasks with the aim of constructing powerful industrial systems. Incentivizing mobile users (MUs) to participate in sensing activities and contribute high-quality data is of paramount importance to the success of MCS services. In this article, we formulate the competitive interactions between a sensing platform (SP) and MUs as a multistage Stackelberg game with the SP as the leader player and the MUs as the followers. Given the unit prices announced by MUs, the SP calculates the quantity of sensing time to purchase from each MU by solving a convex optimization problem. Then, each follower observes the trading records and iteratively adjusts their pricing strategy in a trial-and-error manner based on a multiagent deep reinforcement learning algorithm. Simulation results demonstrate the efficiency of the proposed method.
KW - Cognitive sensor networks
KW - deep reinforcement learning (DRL)
KW - incentive mechanism
KW - mobile crowd sensing (MCS)
KW - multiagent deep deterministic policy gradient (MADDPG)
KW - Stackelberg game
UR - http://www.scopus.com/inward/record.url?scp=85112325791&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.3024611
DO - 10.1109/TII.2020.3024611
M3 - Article
AN - SCOPUS:85112325791
VL - 17
SP - 6182
EP - 6191
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 9
M1 - 9201550
ER -
