Farhad Arshad, Pyun Jae-Young
Wireless and Mobile Communication System Laboratory, Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Sensors (Basel). 2023 Aug 1;23(15):6851. doi: 10.3390/s23156851.
The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.
随着对低功耗、远距离无线通信技术需求的增长，物联网正在迅速发展。长距离广域网（LoRaWAN）就是这样一种技术，近年来因其能够以低功耗提供远距离通信而备受关注。LoRaWAN的主要问题之一是终端设备对无线电资源（如扩频因子和发射功率）的有效利用。为了解决资源分配问题，机器学习（ML）方法已被用于提高LoRaWAN网络性能。本综述论文的主要目的是研究和审视通过先进的ML方法解决的LoRaWAN中的资源管理问题。此外，本综述介绍了可用于数据集收集的公开可用的LoRaWAN框架，讨论了使用建议的ML方法进行高效资源管理所需的特征，并突出了现有的公开可用数据集。该综述还探索和评估了可用于高效资源管理的基于网络模拟器3的ML框架。最后，阐述了关于ML应用在LoRaWAN资源管理中的适用性的未来建议，为有兴趣应用ML来提高LoRaWAN网络性能的研究人员和从业人员提供了全面的指南。
