Profit Maximization in Mobile Crowdsourcing: A Competitive Analysis

Mobile Crowdsourcing, which uses a crowd of workers to complete computer-complexity tasks, has become more and more popular. How to maximize the total profit, which is the difference between total task utility and total worker costs, is a key design goal in such crowdsourcing system. However, this problem is extremely hard because even in the offline situation, this problem has been proved to be NP-hard. In the online situation, we need to consider not only future unknown task arrivals, but also the heterogeneity of both tasks and workers. In this paper, we address this challenging problem from following aspects. To solve this problem, we first build an optimization framework to formulate this problem by explicitly modelling task utility and worker costs. Then, under this framework, we design efficient online assignment algorithms to assign tasks to workers. To analyze the efficiency of our proposed algorithms, we develop a dual-fitting method to analyze both primal objective and dual objective. We prove that our designed algorithms can achieve a constant competitive ratio of 2. Finally, we conduct extensive trace-driven simulations to demonstrate that the online algorithms can outperform baseline algorithms by nearly 20% in terms of the total profit achieved.