Modeling Real-Time Task Assignment for Mobile Crowdsourcing in Opportunistic Networks

Opportunistic network-based mobile crowdsourcing (MCS) outsources location-based human tasks to a crowd of workers, where workers with mobile devices opportunistically have contact with the server. While a number of task assignment algorithms have been proposed for different objectives, real-timeness is not considered. In this article, we are interested in real-time MCS (RT-MCS), in which tasks can be generated at any time step, and task assignment is performed in real-time. We first model an abstract RT-MCS and then instantiate the real-time task assignment problem for opportunistic network-based RT-MCS. A generic real-time task assignment (RTA) algorithm is designed based on the principle of the greedy approach, where each task is assigned to the best worker with the highest expected completion probability. To understand the fundamental performance issues, we formulate closed-form solutions for task completion probability as well as delay. In addition, we identify the critical condition that illuminates the busy state and the not-busy state of an RT-MCS. Furthermore, the analytical and simulation results demonstrate that our analysis yields close approximation of simulation results.