Multi-Attribute Crowdsourcing Task Assignment with Stability and Satisfactory

Abstract

Recently, crowdsourcing applications for smart cities have become more and more popular due to its higher work efficiency and lower work costs. However, the reasonable task assignment is still one of the important challenges for crowdsourcing. The existing researches on crowdsourcing task assignment focus on the tradeoff between maximizing the utility of platforms and minimizing the cost of requesters, but they lack of the considerations of stability and satisfactory. In this paper, we propose an intelligent multi attributes crowdsourcing task assignment with stability and satisfactory, called TASS. TASS can exploit the multi attributes to solve the stability of the transaction, and adopt the game theory to maximize the satisfaction of both sides during the task assignment. Next, we theoretically prove that the task assignment mechanism is truthfulness, individual rationality, stable and satisfactory assignment, and budget-balanced. Finally, we evaluate the performances of TASS with the state-of-the-art task assignment works. The experimental results show that TASS is better than the state-of-the-art task assignment works in terms of truthfulness, individually rationality, stable and satisfactory assignment, and balanced budget.