Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
This addresses energy efficiency and data freshness for low-energy IoT networks, but it is incremental as it builds on existing deep RL and meta-learning techniques.
The study tackled the problem of optimizing UAV flight trajectory and scheduling to minimize age-of-information and transmission power in IoT networks, achieving minimal overall AoI and power with faster convergence and better adaptation than traditional deep RL methods.
Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.