Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose LoRaDRL and provide a detailed performance evaluation. We propose a multi-channel scheme for LoRaDRL. We perform extensive experiments, and our results demonstrate that the proposed algorithm not only significantly improves long-range wide area network (LoRaWAN)'s packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption. Most previous works focus on proposing different MAC protocols for improving the network capacity. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500% in terms of PDR compared to learning-based techniques.

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.