A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing
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College of Computer Science and Technology, Chang Chun University of Science and Technology, Changchun 130022, China
2
Petrochina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4478; https://doi.org/10.3390/s20164478
Received: 18 July 2020
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Revised: 3 August 2020
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Accepted: 7 August 2020
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Published: 11 August 2020
(This article belongs to the Section Intelligent Sensors)
Selection of the optimal users to maximize the quality of the collected sensing data within a certain budget range is a crucial issue that affects the effectiveness of mobile crowdsensing (MCS). The coverage of mobile users (MUs) in a target area is relevant to the accuracy of sensing data. Furthermore, the historical reputation of MUs can reflect their previous behavior. Therefore, this study proposes a coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory for MCS. First, the location information and the historical reputation of mobile users are used to select the optimal users, and the information quality requirement will be satisfied consequently. Second, a two-stage Stackelberg game is applied to analyze the sensing level of the mobile users and obtain the optimal incentive mechanism of the server center (SC). The existence of the Nash equilibrium is analyzed and verified on the basis of the optimal response strategy of mobile users. In addition, mobile users will adjust the priority of the tasks in time series to enable the total utility of all their tasks to reach a maximum. Finally, the EM algorithm is used to evaluate the data quality of the task, and the historical reputation of each user will be updated accordingly. Simulation experiments show that the coverage of the CRJC-IMA is higher than that of the CTSIA. The utility of mobile users and SC is higher than that in STD algorithms. Furthermore, the utility of mobile users with the adjusted task priority is greater than that without a priority order.
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Keywords:
mobile crowdsensing; coverage; historical reputation; Stackelberg game theory; incentive mechanism
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MDPI and ACS Style
Zhang, J.; Yang, X.; Feng, X.; Yang, H.; Ren, A. A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing. Sensors 2020, 20, 4478. https://doi.org/10.3390/s20164478
AMA Style
Zhang J, Yang X, Feng X, Yang H, Ren A. A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing. Sensors. 2020; 20(16):4478. https://doi.org/10.3390/s20164478
Chicago/Turabian StyleZhang, Jing, Xiaoxiao Yang, Xin Feng, Hongwei Yang, and An Ren. 2020. "A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing" Sensors 20, no. 16: 4478. https://doi.org/10.3390/s20164478
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