Low power wide area network (LPWAN) technologies are increasingly catching the attention of the Internet-of-Things market and have brought the need for reliable knowledge about the performance of such networks. This paper is concerned with the performance and scalability of LoRa networks, a leading LPWAN technology. Several recently published articles have analyzed the ability of LoRa networks to scale, i.e., their ability to support increased traffic and number of nodes. This paper proposes to employ message replication and gateways with multiple receive antennas to achieve, respectively, time and spatial diversity. The paper presents the proposed schemes and evaluates them through theoretical analysis and computer simulations. Results show that LoRa networks are highly sensitive to the increase in user and traffic density, but both message replication and multiple antennas can enhance performance. Message replication has an optimum number of message copies for each network configuration, and its utilization is more beneficial in low-density networks, while the use of multiple receive antennas at the gateway is always beneficial.

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by $1.53$ times.

is a remarkable network protocol to support the deployment of Low-Power Wide-Area Network applications for the Internet of Things. Its modulation scheme, , uses different transmission parameters to allow long-range bidirectional communication by trading-off range for time-on-air (ToA). The specification suggests using an Adaptive Data Rate (ADR) algorithm to assign transmission parameters to nodes dynamically. The ADR algorithm used by The Things Network, a global open network, selects transmission parameters for communication without disconnection while lowering ToA and energy consumption. However, its often optimistic link quality estimate drives the assignment of inadequate parameters, especially in lossy channels. More precise channel quality estimates would improve resource allocation at the cost of increased complexity at the network server. Through simulations, we observe that we can use the ADR link margin parameter to compensate for inaccurate link quality estimates, providing better service to nodes and maintaining low levels of energy consumption. In this work, we propose a modification to ADR that allows the selection of the link margin at run-time, increasing the network performance without the need of any prior knowledge and with no increase in the network server computational complexity.

Low Power Wide Area Networks (LPWAN) are wireless connectivity solutions for Internet-of-Things (IoT) applications, including industrial automation. Among the several LPWAN technologies, LoRaWAN has been extensively addressed by the research community and the industry. However, the reliability and scalability of LoRaWAN are still uncertain. One of the techniques to increase the reliability of LoRaWAN is message replication, which exploits time diversity. This paper proposes a novel hybrid coded message replication scheme that interleaves simple repetition and a recently proposed coded replication method. We analyze the optimization of the proposed scheme under minimum reliability requirements and show that it enhances the network performance without requiring additional transmit power compared to the competing replication techniques.

This paper considers the use of successive interference cancellation (SIC) to decode superposed signals in Long Range () networks. We build over a known stochastic geometry model for networks and include the effect of recovering colliding packets through SIC. We derive closed-form expressions for the successful decoding of packets using SIC taking path loss, fading, noise, and interference into account, while we validate the model by means of Monte Carlo simulations. Results show that SIC-enabled networks improve worst-case reliability by up to 34%. We show that, for at least one test scenario, SIC increases by 159% the number of served users with the same worst-case reliability level.

In this work, we apply an asynchronous contention resolution-aided ALOHA protocol to the recently proposed long-range frequency hopping spread spectrum (LR-FHSS) scheme, which is an amendment of widespread chirp spread spectrum (CSS)-based long-range (LoRa) modulation patented by Semtech. More specifically, we modify the so-called asynchronous contention resolution diversity ALOHA (ACRDA) protocol to comply with LR-FHSS requirements and improve LR-FHSS performance against collisions. In the proposed scheme, only the gateway requires modifications, while the ACRDA operation is transparent from the end-devices perspective, which follows the regular LR-FHSS operation. Our results, obtained using a discrete event simulator, show that the proposed scheme outperforms regular LR-FHSS in various aspects, such as average network success (up to 60% improvement in the scenarios considered), goodput ( $2 \times $ better), and number of supported devices for a target reliability level (more than $2 \times $ ). Finally, we also evaluate the impact on the performance of the proposed scheme of parameters, such as the window and the step sizes, showing that a relatively small window size $(\sim 2 \times $ the Time-on-Air of a single message) is sufficient to approach the asymptotic maximum network success achieved by an infinite window.