Incentive mechanism in platform-centric mobile crowdsensing: A one-to-many bargaining approach

In recent years, we have witnessed the explosion of smart devices (eg. smartphones and wearable devices) in our life. These smart devices are more and more powerful with a set of built-in sensors, such as GPS, microphone, camera, gyroscope, accelerometer, and etc. The large-scale and powerful smart devices make the design of mobile crowdsensing applications which will create a new interface between human beings and environments be possible. A key factor to enable such applications is substantial participation of normal smart device users, which requires effective incentive mechanisms. This needs an efficient way to solve the interaction between the task initiator and smart device users. In this paper, we investigate the incentive mechanism for the platform-centric mobile crowdsensing, where the smart devices have resource constraints and their owners also have resource demands. We first give the economic models of the system, then we analyze the interaction between the task initiator and the smart device users by using Nash bargaining theory. More specifically, we formulate the interaction between task initiator and smart device users as a one-to-many bargaining, then we study the bargaining solutions under ordered bargaining and simultaneous bargaining systematically. Finally, we design a distributed algorithm based on dual decomposition method which can not only keep the participators' privacy, but also reduce the sensing-platform's computation load. Extensive numerical experiments have been implemented to verify the efficiency of our incentive mechanism.