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 an intelligent multi-channel resource allocation algorithm for dense LoRa networks termed LoRaDRL and provide a detailed performance evaluation. Our results demonstrate that the proposed algorithm not only significantly improves LoRaWAN's packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption hence increasing both the lifetime and capacity of the network.} Most previous works focus on proposing different MAC protocols for improving the network capacity, i.e., LoRaWAN, delay before transmit etc. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA \textcolor{black}{compared to LoRaSim, and LoRa-MAB 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.
翻译:在未来几年内,IOT装置的计算预期会增加,这促使发展高效算法,有助于有效管理这些装置,同时保持低电耗。在本文中,我们提议为称为LoRaDRL的密集LoRa网络提供智能多渠道资源分配算法,并提供详细的绩效评估。我们的结果表明,拟议的算法不仅大大提高了LoRaWAN的包装交付比率(PDR),而且还能够支持移动终端设备(EDs),同时确保较低的电力消耗量,从而增加网络的寿命和能力。 }过去的大部分工作侧重于提出不同的MAC协议,以提高网络能力,即LoRawAN,延迟传输等。我们表明,通过使用LoRaDRL,我们不仅可以实现同样的效率,而且可以将EDs的复杂程度从基于ED到门户,从而使EDs的寿命和容量增加。 }此外,我们测试LDL在大规模频率干扰攻击中的性能表现,比RDR的学习技术更能改善环境。