LAUSR

With the powerful sensing, computing capabilities of mobile devices, large-scale users with smart devices throughout the city would be the perfect carrier for the people-centric scheme, namely, mobile crowdsensing. Mobile crowdsensing has become a versatile platform for many Internet of things applications in urban scenarios. So how to select the appropriate users to complete the tasks and ensure the quality of the tasks has been a huge challenge for mobile crowdsensing. In this article, we propose a willingness-aware user recruitment strategy based on the task attributes to solve this problem. First, we divide the whole sensing region based on task attributes by a weighted Voronoi diagram and conduct the assessment about the sub-regions according to several parameters, and then categorize sub-regions as hot regions and blank regions. Moreover, we analyze the influence of user willingness on user recruitment and the task completion rate and assess the coverage ability of the users. Finally, we use the greedy method to optimize the user recruitment for each task to select the most suitable users for the tasks. Simulation results show that the willingness-aware user recruitment approach can significantly improve the task completion rate and achieve higher task coverage quality compared with other algorithms.