Age Minimization in Massive IoT via UAV Swarm: A Multi-agent
  Reinforcement Learning Approach
Eslam Eldeeb Mohammad Shehab Hirley Alves
In many massive IoT communication scenarios, the IoT devices require coverage
from dynamic units that can move close to the IoT devices and reduce the uplink
energy consumption. A robust solution is to deploy a large number of UAVs (UAV
swarm) to provide coverage and a better line of sight (LoS) for the IoT
network. However, the study of these massive IoT scenarios with a massive
number of serving units leads to high dimensional problems with high
complexity. In this paper, we apply multi-agent deep reinforcement learning to
address the high-dimensional problem that results from deploying a swarm of
UAVs to collect fresh information from IoT devices. The target is to minimize
the overall age of information in the IoT network. The results reveal that both
cooperative and partially cooperative multi-agent deep reinforcement learning
approaches are able to outperform the high-complexity centralized deep
reinforcement learning approach, which stands helpless in large-scale networks.