With the rapid development of crowdsourcing platforms that aggregate the intelligence of Internet workers, crowdsourcing has been widely utilized to address problems that require human cognitive abilities. Considering great dynamics of worker arrival and departure, it is of vital importance to design a task assignment scheme to adaptively select the most beneficial tasks for the available workers. In this paper, in order to make the most efficient utilization of the worker labor and balance the accuracy of answers and the overall latency, we a) develop a parameter estimation model that assists in estimating worker expertise, question easiness, and answer confidence; b) propose a quality-assured synchronized task assignment scheme that executes in batches and maximizes the number of potentially completed questions (MCQ) within each batch. We prove that MCQ problem is NP-hard and present two greedy approximation solutions to address the problem. The effectiveness and efficiency of the approximation solutions are further evaluated through extensive experiments on synthetic and real datasets. The experimental results show that the accuracy and the overall latency of the MCQ approaches outperform the existing online task assignment algorithms in the synchronized task assignment scenario.

As crowdsourcing has been dramatically investigated and utilized to address problems in the real world, it is essential and important to think about performance optimization. Analogous to computer systems with CPUs, treating each worker as a HPU (Human Processing Unit [1]) and studying the performance optimization on top of HPUs are interesting perspectives to resolve crowdsourcing issues. However, as we characterize HPUs in detail for this purpose, we find that there are significant differences between CPUs and HPUs, leading to the need of completely new optimization algorithms. In this paper, we study the specific optimization problem of obtaining results the fastest for a crowdsourced job with a fixed total budget. In crowdsourcing, jobs are usually broken down into sets of small tasks, which are assigned to workers one at a time. We consider three scenarios of increasing complexity: Identical Round Homogeneous Tasks, Multiplex Round Homogeneous Tasks, and Multiple Round Heterogeneous Tasks. For each scenario, we analyze the stochastic behavior of the HPU clock rate as a function of the remuneration offered. After that, we develop an optimum Budget Allocation Strategy to minimize the latency of the job completion. We validate our results through extensive simulations and experiments on Amazon Mechanical Turk.