Multi-Objective Online Task Allocation in Spatial Crowdsourcing Systems

In this work we aim to provide an efficient solution to the problem of online task allocation in spatial crowdsourcing systems. We focus on the objectives of platform utility maximization and worker utility maximization, yet the proposed schema is generic enough to accommodate more objectives. The goal is to find an allocation of tasks to workers that maximizes the platform's profit and reliability of the results, while simultaneously assigns tasks based on the users' interests to increase user engagement and hence the probability that the users will complete the tasks on time. Our scheme works well in highly fluctuating environments where the tasks to be executed require that the workers meet certain criteria of expertise, availability, reliability, etc. Our detailed experimental evaluation illustrates the benefits and practicality of our approach and demonstrates that our approach outperforms its competitors.