On Task Assignment for Real-Time Reliable Crowdsourcing

With the rapid growth of mobile smartphone users, several commercial mobile companies have exploited crowd sourcing as an effective approach to collect and analyze data, to improve their services. In a crowd sourcing system, "human workers" are enlisted to perform small tasks, that are difficult to be automated, in return for some monetary compensation. This paper presents our crowd sourcing system that seeks to address the challenge of determining the most efficient allocation of tasks to the human crowd. The goal of our algorithm is to efficiently determine the most appropriate set of workers to assign to each incoming task, so that the real-time demands are met and high quality results are returned. We empirically evaluate our approach and show that our system effectively meets the requested demands, has low overhead and can improve the number of tasks processed under the defined constraints over 71% compared to traditional approaches.