  Path-loss modelling in deep-indoor scenarios is a difficult task. On one
hand, the theoretical formulae solely dependent on transmitter-receiver
distance are too simple; on the other hand, discovering all significant factors
affecting the loss of signal power in a given situation may often be
infeasible. In this paper, we experimentally investigate the influence of
deep-indoor features such as indoor depth, indoor distance and distance to the
closest tunnel corridor and the effect on received power using NB-IoT. We
describe a measurement campaign performed in a system of long underground
tunnels, and we analyse linear regression models involving the engineered
features. We show that the current empirical models for NB-IoT signal
attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor
distance and penetration depth do not explain the signal attenuation well and
increase the error of the prediction by 2-12 dB using existing models, and 2) a
promising feature of average distance to the nearest corridor is identified.
