JACIII Vol.25 No.6 pp. 1000-1010
doi: 10.20965/jaciii.2021.p1000


Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model

Liangguang Wu*1,*2, Yonghua Xiong*1,*2,†, Kang-Zhi Liu*3, and Jinhua She*4

*1School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

*2Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

*3Department of Electrical and Electronic Engineering, Chiba University
Chiba 263-8522, Japan

*4School of Engineering, Tokyo University of Technology
1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan

Corresponding author

July 28, 2021
September 8, 2021
November 20, 2021
crowdsensing network, non-cooperative game, task assignment, user incentive, co-evolutionary
Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model

Framework of multi-task crowdsensing platform

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method.

Cite this article as:
Liangguang Wu, Yonghua Xiong, Kang-Zhi Liu, and Jinhua She, “Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.6, pp. 1000-1010, 2021.
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Last updated on Nov. 30, 2021