Delay Optimal UAV Trajectory Planning for Secure Data Collection from Mobile IoT Networks

Abstract

Unmanned aerial vehicles (UAVs) have recently been used in many applications from surveillance to communication. UAVs can also assist the process of data collection from ground Internet of Things (IoT) devices thanks to the low deployment cost and flexibility. Since the energy and flight time of UAVs is limited, the trajectory planning for the UAVs during this data collection process is vital. While there are several studies that look at this problem with varying objectives, there is still a need for finding the optimal UAV path for data collection from mobile IoT devices with both the delay and secure collection of data in mind as the main concern. In this work-in-progress paper, we study this problem where a UAV aims to minimize the average or maximum delay of the collected data from ground IoT devices within its flight duration while also staying away from potential eavesdroppers on its path. We model the problem using Integer Linear Programming (ILP) and present results for different scenarios. Our next goal is to develop a reinforcement learning based solution that can provide results that are close to optimal ILP based results but also applicable to real-life scenarios.