This papers uses stochastic geometry and queuing
theory to study he scalability of long-range (LoRa) networks,
accounting for duty cycling restrictions and imperfect spreading
factor (SFs) orthogonality. The scalability is characterised by the
joint boundaries of device density and traffic intensity per device.
Novel cross-correlation factors are used to quantify imperfect SForthogonality. Our results show that a proper characterisation
of LoRa orthogonality extends the scalability of the network.
They also highlight that for low/medium densities decreasing
the SF extends the spanned spectrum of sensing applications
characterised by their traffic requirements (i.e. sensing rate).
However, for high density (> 104 nodes/Km2
), the Pareto frontiers
converge to a stability limit governed by the SF allocation scheme
and the predefined capture thresholds. The results further evince
the importance of capturing threshold distribution among the SFs
to mitigate the unfair latency.