When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

Jonathan Rodriguez, Zhenyu Zhou, Junhao Feng, Bo Gu, Bo Ai, Shahid Mumtaz, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review


With the increasing popularity of unmanned aerial 2 vehicles (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 and 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 paper, we consider 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 or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Communications
Publication statusPublished - 19 Jul 2018


  • Mobile Crowd Sensing
  • Unmanned Aerial Vehicles
  • Energy Efficiency
  • Route Planning


Dive into the research topics of 'When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning'. Together they form a unique fingerprint.

Cite this