PHY/MAC Uplink Performance of LoRa Class A Networks

Furtado, António; Pacheco, João; Oliveira, Rodolfo

doi:10.1109/jiot.2020.2974429

Cited by 30 publications

(11 citation statements)

References 37 publications

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“…However, they do not include duty cycle constraints and all nodes are required to use the same transmission power. Furtado et al [35] consider a model to estimate the delivery ratio in LoRa networks. Their model assumes that nodes are uniformly distributed around the gateway and that gateways can decode multiple frames transmitted with the same spreading factor (which is not currently supported by LoRa chipsets).…”

Section: Related Workmentioning

confidence: 99%

Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

Toro-Betancur

Premsankar

Słabicki

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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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.

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Section: Related Workmentioning

confidence: 99%

Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

Toro-Betancur

Premsankar

Słabicki

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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show abstract

“…User scheduling was incorporated to a multi channel LoRa network in [29] to improve the synchronization packet length. The average number of decoded LoRa frames was investigated in [30] by taking into account physical layer and medium access control. In [32], the devices are powered by energy harvesting sources for uplink transmission considering only one channel.…”

Section: Related Workmentioning

confidence: 99%

LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

Hamdi,

Baccour,

Erbad

et al. 2021

Preprint

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LoRa wireless networks are considered as a key enabling technology for next generation internet of things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this paper, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This paper proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system's quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on Reinforcement Learning (RL), are developed. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

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“…Current studies are mostly based on the results obtained through simulations [ 13 , 14 ], and imply that the use of multiple transmission channels and spreading factors lead to a system that can be deemed as a superposition of independent sub-systems (featuring one channel, with one single SF) [ 15 ]. This is only a powerful simplification, especially when the SFs adopted by LoRa are quasi-orthogonal [ 16 ]; and as a consequence, in certain conditions, the collisions can prevent the correct reception of overlapped transmissions by using different SFs.…”

Section: Lora Modulation Evaluationmentioning

confidence: 99%

LoRa Traffic Generator Based on Software Defined Radio Technology for LoRa Modulation Orthogonality Analysis: Empirical and Experimental Evaluation

Lavric¹,

Petrariu²,

Coca³

et al. 2020

Sensors

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The digital revolution has changed the way we implement and use connected devices and systems by offering Internet communication capabilities to simple objects around us. The growth of information technologies, together with the concept of the Internet of Things (IoT), exponentially amplified the connectivity capabilities of devices. Up to this moment, the Long Range (LoRa) communication technology has been regarded as the perfect candidate, created to solve the issues of the IoT concept, such as scalability and the possibility of integrating a large number of sensors. The goal of this paper is to present an analysis of the communication collisions that occur through the evaluation of performance level in various scenarios for the LoRa technology. The first part addresses an empirical evaluation and the second part presents the development and validation of a LoRa traffic generator. The findings suggest that even if the packet payload increases, the communication resistance to interferences is not drastically affected, as one may expect. These results are analyzed by using a novel Software Defined Radio (SDR) technology LoRa traffic generator, that ensures a high-performance level in terms of generating a large LoRa traffic volume. Despite the use of orthogonal variable spreading factor technique, within the same communication channel, the collisions between LoRa packets may dramatically decrease the communication performance level.

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PHY/MAC Uplink Performance of LoRa Class A Networks

Cited by 30 publications

References 37 publications

Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

LoRa Traffic Generator Based on Software Defined Radio Technology for LoRa Modulation Orthogonality Analysis: Empirical and Experimental Evaluation

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