Data Collection in Laser-Powered UAV-Assisted IoT Networks: Phased Scheme Design Based on Improved Clustering Algorithm

Abstract

Owing to the striking features, such as controllable mobility, low cost, and so on, unmanned aerial vehicles (UAVs) are deemed to be the promising solution to complete data collection tasks of Internet of Things devices (IoTDs). The limited onboard energy, however, undeniably impedes the progress of collecting data. Furthermore, this task is complicated further due to the various amount of data generated by the different types of IoTDs. The goal of this paper is to design an applicable data collection scheme for IoT networks using a laser-powered UAV to maximize system energy efficiency. We propose an improved clustering algorithm called logarithm kernel-based mean shift (LKMS) inspired by the idea behind the mean shift algorithm. Based on the LKMS, we propose a novel algorithm to determine the optimal visiting order and enter points (EPs) of IoTD clusters, paving the way for the following optimization. To manage to solve the variables-coupling and non-convex formulated problem, we artificially divide the entire flying procedure into two phases, the flying and charging (FC) phase as well as the collecting data (CD) phase, depending on whether the UAV is harvesting energy. The block coordinate descent (BCD) and the successive convex approximation (SCA) methods are used to decouple the variables and solve the non-convex subproblems. Simulation results validate the effectiveness of our proposed scheme.