Energy-Efficient Mobile Crowd Sensing Based on Unmanned Aerial Vehicles

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). However, the on- board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we investigate the joint optimization of route planning and task assignment for UAV-aided MCS from an energy efficiency perspective. The formulated NP-hard problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on dynamic programming (DP), and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley (GS) algorithm. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.