Prediction-Based Task Allocation in Mobile Crowdsensing

Due to the fast development of intellectual devices and wireless technology, the mobile crowdsensing (MCS) technology has gradually turn into an effective means of sensing and collecting information about the surrounding environment in real-time. Task allocation is one of the essential questions in MCS. Previous task assignment methods only directly assign the task to the workers nearby the tasks, without paying attention to the location change of the workers and the tasks, which increase workers' traveling cost and platform cost. Instead, our goal in this paper is to consider predicted workers and tasks to improve overall utility. The dynamic task allocation proposed by us is a novel optimization problem. To tackle this problem, the paper leverages the semi-Markov model to predict the position distribution of workers and tasks. Moreover, the connection probability between worker and task is calculated under time constraint. Eventually, we design a prediction-based task allocation algorithm (PBTA), which can derive the maximum overall system utility and lowest traveling cost. Through experiments, we evaluate our approach extensively using two large-scale real-world datasets. The experimental results validate the effectiveness and efficiency of our proposed scheme.