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An Incentive Mechanism-based Stackelberg Game for Scheduling of LoRa Spreading Factors
IEEE Transactions on Network and Service Management ( IF 5.3 ) Pub Date : 2020-12-01 , DOI: 10.1109/tnsm.2020.3027730
Preti Kumari , Hari Prabhat Gupta , Tanima Dutta

Wireless Local Area Networks (WLANs) are one of the most popular networks for the Internet-of-Things (IoT) applications. Among various WLAN technologies, the Long-Range WAN (LoRaWAN) has gained a high demand in recent years because of its low power consumption and long-range communication. However, the Long-Range (LoRa) network suffers from interference problem among LoRa Devices (LDs) that are connected to the LoRa gateway by using the same Spreading Factors (SFs). In this article, we propose a game theory-based approach for estimating the time duration of transmission of data on suitable SFs such that interference problem is reduced and network devices maximize their utilities. We next propose a scheduling algorithm that schedules the allocated time duration on the SFs such that the waiting time of the network can be minimized. We finally use the network simulator-3 for validating the propose work. Various experiments are performed which demonstrate the improvement in the network performance.

中文翻译:

基于激励机制的 LoRa 传播因子调度 Stackelberg 博弈

无线局域网 (WLAN) 是最流行的物联网 (IoT) 应用网络之一。在各种无线局域网技术中,远程广域网(LoRaWAN)由于其低功耗和远距离通信的特点,近年来获得了很高的需求。然而,远程 (LoRa) 网络存在使用相同扩频因子 (SF) 连接到 LoRa 网关的 LoRa 设备 (LD) 之间的干扰问题。在本文中,我们提出了一种基于博弈论的方法,用于估计在合适的 SF 上传输数据的持续时间,从而减少干扰问题并使网络设备最大化其效用。我们接下来提出了一种调度算法,该算法在 SF 上调度分配的持续时间,从而可以最小化网络的等待时间。我们最终使用网络模拟器 3 来验证提议的工作。进行了各种实验,证明了网络性能的提高。
更新日期:2020-12-01
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