Minimizing communication-computing energy consumption for UAV assisted collaborative computing offloading

Unmanned aerial vehicles (UAVs) are viewed as a potential technology for handling user offloading duties as edge nodes. With their adaptable qualities, UAVs may be quickly deployed to useful locations and service consumers. However, the inability of UAVs to operate continuously for an extended time is a challenge for the current UAV-assisted mobile edge computing solutions. We put forth an optimization problem that involves the dynamic division of computational windows for UAVs, the optimization of user grouping and user transmission power, and the optimization of UAV deployment locations to save energy. We design a Communication-Computing Resource Scheduling with Dynamic computational Window allocation (CCRS-DW) algorithm to realize the problem decomposition and optimization. Specifically, the $K$-means clustering technique and the bisection search are used to tackle this problem. Simulation results show that the energy consumption of the proposed CCRS-DW scheme is significantly lower than that of other benchmark schemes.