Task allocation based on profit maximization for mobile crowdsourcing

In recent years, with the development of smart devices, mobile users can use them to sense the environment. In order to improve the data quality and achieve maximum profits, incentive mechanism is needed to motivate users to participate. In this paper, reputation mechanism, participant selection, task allocation and joint pricing in mobile crowdsourcing system are studied. A user reputation evaluation method is proposed, and a participant selection algorithm (PSA) based on user reputation is proposed. Besides, a social welfare maximization algorithm (SWMA) is proposed, which achieves task pricing with maximizing the interests of all parties, including both task publishers and mobile users. The social welfare maximization problem is divided into local optimization sub-problems which can be solved by double decomposition. It is proved that the algorithm converges to the optimal solution. Results of simulations verify that algorithms PSA and SWMA are effective.