Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge

TL;DR

This paper introduces a deep reinforcement learning approach for optimizing UAV trajectories to minimize Age of Information and energy consumption in IoT networks, considering UAV battery constraints and recharging logistics.

Contribution

It presents a novel deep Q-network based method for joint UAV path planning that accounts for battery life and recharging, improving AoI and energy efficiency.

Findings

Lower ergodic age compared to benchmarks

Reduced energy consumption with the proposed method

Fast convergence of the deep Q-network algorithm

Abstract

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).