Space-time low complexity algorithms for scheduling a fleet of UAVs in smart cities using dimensionality reduction approaches

In this paper, we propose three low complexity algorithms to solve a scheduling framework problem for Unmanned Aerial Vehicles (UAVs) in smart cities. The objective is to assign UAVs to different missions having different characteristics such as geographical locations, starting times, and duration while minimizing the total energy consumption and ensuring sequential and parallel mission execution. A mixed integer linear programming is formulated and solved using the proposed algorithms, which employ dimensionality reduction techniques to decrease the computational complexity. In this paper, we describe the UAV scheduling problem as well as the developed algorithms. Significant computational saving has been achieved with the different proposed algorithms. In the selected simulation results, we evaluate the advantages and limitations of the algorithms and compare their performances to the ones of the optimal branch-and-bound-based solution.