An Incentive Mechanism-Based Stackelberg Game for Scheduling of LoRa Spreading Factors

Abstract

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.