Abstract
With increasing popularity of UAVs, it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this chapter, we demonstrate the fixed-wing UAV-aided MCS system, and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization 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 either dynamic programming (DP) or genetic algorithms (GA), and the task assignment problem is addressed in the second stage by exploring the GS algorithm.
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