Data Delivery Efficient Spreading Factor Allocation in Dense LoRaWAN Deployments

Gusev, O. B.; Turlikov, Andrey; Kuzmichev, S. A.; Stepanov, Nikita

doi:10.1109/redundancy48165.2019.9003343

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…With the aim of improving the probability of data delivery in LoRaWAN, the authors in [11] and [12] highlight the shortcomings of legacy SF assignment techniques and propose algorithms to address them. While the authors in the former claim that they can improve the scalability of LoRaWAN with a small increase in the end devices' power consumption, the authors in the latter illustrate that they can improve data delivery in dense LoRaWAN deployments.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Cooperation for Spreading Factor Assignment in a Multioperator LoRaWAN Deployment

Fawaz

Khawam

Lahoud

et al. 2021

IEEE Internet Things J.

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Faced with the limitations of the Aloha random access scheme and spread spectrum techniques, LoRaWAN is yet to realize its potential as the flagship technology for largescale Internet of Things applications. LoRaWAN allows for low power and long range communications. Nonetheless, concurrent transmissions on the same spreading factors, increased with the inevitable densification of device deployment, will lead to collisions and degradation in performance. The problem is further amplified due to the shortage in radio resources, with multiple operators utilizing the same unlicensed frequency bands. In this paper, we investigate different inter-operator cooperation schemes and devise multiple algorithms for spreading factor assignment in a multi-operator LoRaWAN deployment scenario. We start by proposing a proportional fair optimal formulation for the assignment with the objective of maximizing the logarithmic sum of the normalized throughput per spreading factor. Under the assumption of partial operator cooperation, we propose a gradient ascent based iterative algorithm for solving the spreading factor assignment problem, and a game theory based approach, wherein each network operator seeks to maximize its own normalized throughput. Finally, and with cooperation between different operators bound to be limited, we use recurrent neural networks to enable the prediction of the success rate per spreading factor. This prediction allows the different operators to assign spreading factors with minimum cooperation. We simulate our proposals and compare them to the legacy LoRaWAN approach as well as others in the state-of-the-art, highlighting the gains they produce in terms of total normalized throughput and packet delivery ratios.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Cooperation for Spreading Factor Assignment in a Multioperator LoRaWAN Deployment

Fawaz

Khawam

Lahoud

et al. 2021

IEEE Internet Things J.

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show abstract

“…Iterating the algorithm i.e., solving again for SF assignment after channel assignment and so on does not yield a different result. This is because, for our objective, the SF selection is independent of what channels the devices are using, as attested to in Equation (7). Finally, the joint assignment algorithm can either be semi-distributed, by using the best response algorithms to solve for the NEs, or completely distributed, by using the learning approach.…”

Section: Optimal Formulation For Sf and Channel Assignmentmentioning

confidence: 99%

“…With the objective of increasing the data delivery ratio in LoRaWAN, the authors of [ 2 , 7 ] detail the limitations of the legacy SF selection algorithm and, thereafter, propose algorithms to tackle them. In [ 2 ], the authors argue that their approach can improve the scalability of LoRaWAN with a trade-off of a marginal increase in device power consumption.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…In [ 2 ], the authors argue that their approach can improve the scalability of LoRaWAN with a trade-off of a marginal increase in device power consumption. In [ 7 ], the authors claim that their proposed algorithm can improve data delivery in a dense LoRaWAN deployment.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…In this paper, we consider multiple co-located network operators, unlike the vast majority of the state-of-the-art that work on single-cell, single operator networks [ 2 , 4 , 5 , 7 , 9 , 10 , 12 ]. This invalidates many of the state-of-the-art proposals that presume that their algorithms can be addressed in a centralized manner, an unrealistic assumption in a multi-operator deployment.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

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Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments

Fawaz

Khawam

Lahoud

et al. 2020

Sensors

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LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

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Minimizing the Peak Age of Information in LoRaWAN System Based on the Importance of Information

Kim,

Turlikov,

Markovskaya

et al. 2024

Lecture Notes in Computer Science

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Data Delivery Efficient Spreading Factor Allocation in Dense LoRaWAN Deployments

Cited by 9 publications

References 11 publications

Cooperation for Spreading Factor Assignment in a Multioperator LoRaWAN Deployment

Cooperation for Spreading Factor Assignment in a Multioperator LoRaWAN Deployment

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments

Minimizing the Peak Age of Information in LoRaWAN System Based on the Importance of Information

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