Open Access

ARTICLE

Research on Crowdsourcing Price Game Model in Crowd Sensing

Weijin Jiang^1,2, Xiaoliang Liu^1,2,*, Dejia Shi¹, Junpeng Chen^1,2, Yongxia Sun^1,2, Liang Guo³

1 School of Computer Science and Technology, Hunan University of Technology and Business, Changsha, 410205, China
2 Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Changsha, 410205, China
3 School of Bioinformatics, University of Minnesota, Twin Cities, 15213, USA

* Corresponding Author: Xiaoliang Liu. Email:

Computers, Materials & Continua 2021, 68(2), 1769-1784. https://doi.org/10.32604/cmc.2021.016609

Received 06 January 2021; Accepted 11 February 2021; Issue published 13 April 2021

Abstract

Crowd-Sensing is an innovative data acquisition method that combines the perception of mobile devices with the idea of crowdsourcing. It is a new application mode under the development of the Internet of Things. The perceptual data that mobile users can provide is limited. Multiple crowdsourcing parties will share this limited data, but the cost that the crowdsourcing party can pay is limited, and enough mobile users are needed to complete the perceptual task, making the group wisdom is really played. In this process, there is bound to be a game between the crowds and the mobile users. Most of the existing researches consider a group-aware system. A group of mobile users will directly share or compete for the opportunity of the crowd-holders to do tasks and get paid, the behavior of multiple crowd-source parties, and their bilateral interaction with mobile users. The research is not clear enough and there is no targeted research. This paper will model and analyze the dynamic evolution process of crowd sensing perception. Based on the unique characteristics of crowd-source non-cooperative game and crowd-sourced Nash equilibrium, we will develop a perceptual plan for mobile users and use the stability analysis of iterative algorithms to explore a way to better match the capabilities of mobile users and the needs of crowdsourced parties. Our theoretical analysis and simulation results verify the dynamic evolution model of crowdsourcing in group perception and propose a method to improve the efficiency of crowdsourcing.

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Keywords

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