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 widearea 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 distancebased 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
