Minimizing Age of Information in UAV-Assisted Data Collection With Limited Charging Facilities

Age of Information (AoI) has become a new metric for data freshness in Unmanned Aerial Vehicle (UAV)-assisted data collection. Whereas, the UAVs are usually energy-limited. When they leave to be charged, the AoI may increase additionally. Current studies of optimizing the AoI usually consider the situation where charging facilities are sufficient, which is not true in many scenarios. For the situation where only one UAV can be charged at one time, we propose a multi-agent cooperative Deep Q-Network algorithm with delayed reward (DR-MADQN) to minimize the AoI. Simulation results show DR-MADQN can effectively decrease the AoI and charge waiting time.

I. Introduction

Unmanned Aerial Vehicles (UAVs) have been widely used to collect sensory data in various Internet of Things (IoT) applications, such as environmental monitoring [1], disaster management [2] and military reconnaissance. In such UAV-assisted data collection, Age of Information (AoI) is usually used to measure the freshness of the sensory data, which is an important metric in latency sensitive applications. Consequently, scheduling the data collection strategy of the UAVs to optimize AoI has become a crucial problem.