Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
This addresses the challenge of efficient data collection in massive IoT networks for applications requiring timely updates, though it is incremental as it applies existing multi-agent methods to a specific domain.
The paper tackled the problem of minimizing age of information in massive IoT networks using UAV swarms, and found that cooperative and partially cooperative multi-agent deep reinforcement learning approaches outperformed centralized methods, which failed in large-scale scenarios.
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.