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Bringing Fairness in LoRaWAN through SF Allocation Optimization

Christelle Caillouet 1 Martin Heusse 2 Franck Rousseau 2
1 COATI - Combinatorics, Optimization and Algorithms for Telecommunications
Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués, CRISAM - Inria Sophia Antipolis - Méditerranée
2 Drakkar
LIG - Laboratoire d'Informatique de Grenoble
Abstract : We propose an optimization model for single-cell LoRaWAN planning which computes the limit range of each spreading factor (SF) in order to maximize the minimum packet delivery ratio (PDR) of every node in the network. It allows to balance the opposite effects of attenuation and collision of the transmissions and guarantee fairness among the nodes. We show that our optimization framework improves the worst PDR of the nodes by more than 13 percentage points compared to usual SF boundaries based on SNR threshold. A study of the tradeoff between precision and resolution time of the model shows its effectiveness even with a small number of possible distance limits, and its scalability when the node density increases.
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Submitted on : Thursday, June 4, 2020 - 5:42:41 PM
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Christelle Caillouet, Martin Heusse, Franck Rousseau. Bringing Fairness in LoRaWAN through SF Allocation Optimization. ISCC 2020 - 25th IEEE Symposium on Computers and Communications, Jul 2020, Rennes, France. ⟨10.1109/ISCC50000.2020.9219611⟩. ⟨hal-02780468⟩



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