Joint 3-D Trajectory and Resource Optimization in Multi-UAV-Enabled IoT Networks With Wireless Power Transfer

This article studies the data collection problem in an Internet-of-Things (IoT) network with multiple unmanned aerial vehicles (UAVs) where UAVs first power multiple IoT devices by wireless power transfer, and then IoT devices utilize the harvested energy to transmit data to UAVs. Different from most of the existing works that often assume the channel between the UAV and the IoT device is a simplified Line-of-Sight (LoS) channel, a more practical and accurate probabilistic LoS channel model is adopted, in which both the elevation angle and the distance between the UAV and the IoT device determine the channel gain. Our objective is to maximize the UAV's minimum data collection rate among all IoT devices by jointly optimizing time allocation and 3-D trajectory of UAVs within a limited time duration. This results in a nonconvex optimization problem, which is challenge to solve. To tackle this difficulty, we transform the nonconvex problem to a difference of convex (D.C.) optimization problem by subtly using several methods. To solve the D.C. optimization problem, an efficient iterative algorithm is designed via a successive convex approximation method. Numerical simulation results are provided to verify the performance of the proposed algorithm compared to two benchmark algorithms, the algorithm with simplified LoS model and that with 2-D trajectory optimization, under various conditions.

Automated summary

This article studies the data collection problem in an Internet-of-Things network with multiple unmanned aerial vehicles (UAVs) and proposes an optimization algorithm to maximize the minimum data collection rate of UAVs among all IoT devices, by jointly optimizing time allocation and 3-D trajectory of UAVs. The proposed algorithm transforms the nonconvex problem into a difference of convex optimization problem and is validated through numerical simulations against benchmark algorithms under various conditions.