Adaptive algorithm for spreading factor selection in LoRaWAN networks with multiple gateways

Recently, LoRaWAN has been considered a promising technology for large-scale IoT applications owing to its ability to achieve low power and long range communications. However, LoRaWAN is limited using Aloha random access scheme. When in dense scenarios, such scheme leads to a high number of collisio...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2020-12, Vol.182, p.107491, Article 107491
Main Authors: Loubany, Ali, Lahoud, Samer, El Chall, Rida
Format: Article
Language:English
Subjects:
Adaptive algorithm
Adaptive algorithms
Algorithms
Aloha
Capture effect
Computer networks
Densification
Gateways
Internet of thing
LoRaWAN
Optimization
Random access
Scalability
Signal to noise ratio
Spreading factors
System reliability
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Summary:Recently, LoRaWAN has been considered a promising technology for large-scale IoT applications owing to its ability to achieve low power and long range communications. However, LoRaWAN is limited using Aloha random access scheme. When in dense scenarios, such scheme leads to a high number of collisions, thus severely impacts the reliability and scalability of LoRaWAN. In this paper, we investigate the impact of scalability and densification of nodes and gateways on the system reliability taking into account the capture effect. We propose an optimization problem to derive the node distribution at different spreading factors (SF) in LoRaWAN networks with multiple gateways. We then introduce an adaptive algorithm that enables to easily implement SF optimization by adjusting the signal-to-noise ratio thresholds. Moreover, the performance of the proposed algorithm is compared with the performance of legacy LoRaWAN and relevant algorithms from the state-of-the-art. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms, and improves the throughput and packet delivery ratio of the network.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2020.107491