Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

Abstract

Long Range (LoRa) is a low-power wireless communication technology for long-range connectivity, extensively used in the Internet of Things. Several works in the literature have analytically characterized the performance of LoRa networks, with particular focus on scalability and reliability. However, most of the related models are limited, as they cannot account for factors that occur in practice, or make strong assumptions on how devices are deployed in the network. This article proposes an analytical model that describes the delivery ratio in a LoRa network with device-level granularity. Specifically, it considers the impact of several key factors that affect real deployments, including multiple gateways and channel variation. Therefore, the proposed model can effectively evaluate the delivery ratio in realistic network topologies, without any restrictions on device deployment or configuration. It also accurately characterizes the delivery ratio of each device in a network, as demonstrated by extensive simulations in a wide variety of conditions, including diverse networks in terms of node deployment and link-level parameter settings. The proposed model provides a level of detail that is not available in the state of the art, and it matches the simulation results within an error of a few percentage points.

Learning How to Configure LoRa Networks With No Regret: A Distributed Approach

“…The goal of this article is to maximize the delivery ratio of each node by choosing appropriate SFs and TPs. The delivery ratio of each node is described by using the model in [5], which is accurate enough to include the most important factors affecting real-world networks -including the capture effect, channel variations, and duty-cycle restrictions. The capture effect states that one out of multiple packets simultaneously received at a destination can be successfully decoded -specifically, the packet whose receive power is higher than the others by the SF-dependent thresholds given in [6,Table II].…”

“…These thresholds show that different SFs are not fully orthogonal, namely, that packets transmitted with different SFs have a small probability of interfering with each other; for this reason, SFs are called quasi-orthogonal. The packet generation of nodes follows a Poisson distribution as it represents the discrete and random nature of transmissions in LPWANs [3,5]. Radio propagation follows the log-distance path loss model.…”

“…Radio propagation follows the log-distance path loss model. Accordingly, the delivery ratio of node n is given by [5]:…”

“…The SF trades-off communication range and data rate [4]; a high SF results in a long communication range at a low data rate. The TP not only determines the coverage range of the node, but also affects the probability of collision between two nodes [5], as a higher TP achieves a longer communication range. Furthermore, LoRa networks are subject to the capture effect [6]: depending on its receive power, a packet can be successfully decoded by the gateway even in presence of collisions.…”

LoRaWAN Behaviour Analysis through Dataset Traffic Investigation

“…Packet loss in this kind of network can be caused by a variety of factors. In particular, research efforts have been carried out to derive a model for packet collision at the LoRa physical layer [ 14 , 15 ].…”