No abstract

This paper considers a scenario in which an unmanned aerial vehicle (UAV) collects the sensed data from the terrestrial Internet of things (IoT) devices in an untraveled and base station (BS)-uncovered area.These IoT devices, in this scenario, are of various types and deployed in a spare manner.Consequently, how to design an effective and suitable resource allocation scheme is a key part of dispatching a UAV to collect the sensed data from IoT devices.The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices' communication scheduling, UAV trajectory, and bandwidth allocation.To facilitate data collection by UAVs, a logarithmic kernel-based mean shift (LKMS) clustering algorithm is proposed to group IoT devices into multiple clusters.Based on the clustering result, a mixed-integer joint non-convex problem is formulated.To avoid the difficulties caused by solving the aforementioned problem directly, the block coordinate descent (BCD)-based method, as an alternative, is adopted to decouple the variables and decompose the non-convex problem.To tackle the non-convex subproblems, a successive convex approximation (SCA)-based algorithm is also proposed.Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV-assisted wireless IoT networks to collect a huge amount of data.