Almarzoqi Samar Adel, Yahya Ahmed, Matar Zaki, Gomaa Ibrahim
Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt.
Computers & Systems Department, National Telecommunication Institute (NTI-Egypt), Ministry of Communications and Information Technology, Cairo 11112, Egypt.
Sensors (Basel). 2022 Feb 18;22(4):1603. doi: 10.3390/s22041603.
Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work's focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts' advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network's performance in terms of data throughput and power consumption.
长距离广域网（LoRaWAN）是一种用于标准物联网（IoT）低功耗广域网（LPWAN）的开源协议。这项工作的重点是LoRa多臂赌博机分散决策解决方案。本文的贡献在于研究带有先前专家建议的重新学习EXP3多臂赌博机（MAB）算法对LoRaWAN网络性能的影响。LoRa智能节点具有一种自我管理的EXP3算法，用于根据其观察结果选择和更新传输参数。在为置信度更新不同选择之前，最佳参数选择需要先前关联的分布建议（专家）。本文提出了一种新方法来研究每个传输参数的组合专家分布对LoRaWAN网络性能的影响。以优化功耗成功传输数据包是本文的关键。仿真结果的验证证明，组合专家分布在数据吞吐量和功耗方面提高了LoRaWAN网络的性能。
