EWS: Exponential Windowing Scheme to Improve LoRa Scalability

Abstract

Internet-of-Things (IoT) applications require a network that covers a large geographic area, consumes less power, is low-cost, and is scalable with an increasing number of connected devices. Low-power wide-area networks (LPWANs) have recently received significant attention to meet these requirements of IoT applications. Long-range wide-area network (LoRaWAN) with long range (LoRa) (the physical layer design for LoRaWAN) has emerged as a leading LPWAN solution for IoT. However, LoRa networks suffer from the scalability issue when supporting a large number of end devices that access the shared channels randomly. The scalability of LoRa networks greatly depends on the spreading factor (SF) allocation schemes. In this article, we propose an exponential windowing scheme (EWS) for LoRa networks to improve the scalability of LoRa networks. EWS is a distance-based SF allocation scheme. It assigns a distance parameter to each SF to maximize the success probability of the overall LoRa network. Using stochastic geometry, expressions for success probability are derived under co-SF interference. The impact of exponential windowing and packet size is analyzed on packet success probability. In addition, the proposed scheme is compared with the existing distance-based SF allocation schemes: equal-interval-based and equal-area-based schemes, and it is shown that the proposed scheme performs better than the other two schemes.