(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
This addresses resource allocation for IoT systems, but it is incremental as it applies existing DRL methods to a new practical context.
The paper tackles resource allocation in distributed IoT systems by proposing a deep reinforcement learning framework trained with real-world data, demonstrating feasibility and effectiveness in terms of Frame Success Rate.
Deep Reinforcement Learning (DRL) has emerged as an efficient approach to resource allocation due to its strong capability in handling complex decision-making tasks. However, only limited research has explored the training of DRL models with real-world data in practical, distributed Internet of Things (IoT) systems. To bridge this gap, this paper proposes a novel framework for training DRL models in real-world distributed IoT environments. In the proposed framework, IoT devices select communication channels using a DRL-based method, while the DRL model is trained with feedback information. Specifically, Acknowledgment (ACK) information is obtained from actual data transmissions over the selected channels. Implementation and performance evaluation, in terms of Frame Success Rate (FSR), are carried out, demonstrating both the feasibility and the effectiveness of the proposed framework.