A crowdsensing market based on game theory: participant incentive, task assignment and pricing guidance
Liangguang Wu, Yonghua Xiong, Kang Zhi Liu, Jinhua She · International Journal of Communication Networks and Distributed Systems · 2022
The development of 5G and internet of things technologies has promoted the application of crowdsensing services. Consequently, online crowdsensing markets, based on data trade, have emerged. In this article, we first investigate the status quo of the current crowdsensing and crowdsourcing markets, then analyse behavioural characteristics among participants. Thereafter, we design a unified crowdsensing market framework based on supply and demand. These are aimed at encouraging mobile users and data requesters to participate in market activities, with special attention paid to participant incentive models affected by the market environment. Next, we formally consider several new features in the crowdsensing service to provide iterative methods for solving sensing strategy of all participants, with the aim of achieving Nash equilibrium, including task assignment for mobile users and price guidance for data requesters. We validate our proposed methods by providing some numerical results, and discuss several challenges and open issues to be solved.