(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems

TL;DR

This paper introduces a novel framework for training Deep Reinforcement Learning models with real-world data to optimize resource allocation in distributed IoT systems, demonstrating improved communication efficiency.

Contribution

It presents a new framework for training DRL models with real-world feedback in distributed IoT environments, addressing a gap in practical deployment.

Findings

Framework is feasible with real-world data

DRL-based channel selection improves Frame Success Rate

Effective in distributed IoT communication scenarios

Abstract

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.