Quality-aware incentive mechanism based on payoff maximization for mobile crowdsensing

Abstract

Recent years, we have witnessed the explosion of smart devices. These smart devices are more and more powerful with a set of built in sensor devices, such as GPS, accelerometer, gyroscope, camera, etc. The large scale and powerful smart devices make the mobile crowdsensing applications which leverage public crowd equipped with various mobile devices for large scale sensing tasks be possible. In this paper, we study a critical problem of payoff maximization in mobile crowdsensing system with incentive mechanism. Due to the influence of various factors (e.g. sensor quality, noise, etc.), the quality of the sensed data contributed by individual users varies significantly. Obtaining the high quality sensed data with less expense is the ideal of sensing platforms. Therefore, we take the quality of individuals which is determined by the sensing platforms into incentive mechanism design. We propose to maximize the social welfare of the whole system, due to that the private parameters of the mobile users are unknown to the sensing platforms. It is impossible to solve the problem in a central manner. Then a dual decomposition method is employed to divide the social welfare maximization problem into sensing platforms’ local optimization problems and mobile users’ local optimization problems. Finally, distributed algorithms based on an iterative gradient descent method are designed to achieve the close-to-optimal solution. Extensive simulations demonstrate the effectiveness of the proposed incentive mechanism.