An incentive mechanism design for multitask and multipublisher mobile crowdsensing environment

Smartphones and mobile networks have created a new paradigm called mobile crowdsensing for data gathering about a large-scale phenomenon. However, in a multitask and multi-publisher environment, user participation in tasks plays a crucial role in their success due to competition. An effective way is to provide incentives to users. This paper presents an incentive mechanism design for a multitask and multi-publisher mobile crowdsensing system based on the game theory and Stackelberg game. We aim to determine a sustainable strategy for distributing incentives between users performing tasks to multiple publishers. We study the publisher's optimal rewards for its tasks to maximize its profitability in competition with other publishers. The existence of a unique Nash equilibrium is proved, and a distributed algorithm has also been proposed to specify this equilibrium point. Extensive simulations of the mechanism and its convergence to the Nash equilibrium are conducted. The performance evaluation has revealed that this solution has the required efficiency and scalability; the proposed algorithm also converged to the game's equilibrium point.

Automated summary

This paper presents an incentive mechanism design for a multitask and multi-publisher mobile crowdsensing system based on game theory and Stackelberg game. The optimal rewards for tasks and the existence of a unique Nash equilibrium have been studied, and a distributed algorithm has been proposed to specify this equilibrium point.