  Urban LoRa networks promise to provide a cost-efficient and scalable
communication backbone for smart cities. One core challenge in rolling out and
operating these networks is radio network planning, i.e., precise predictions
about possible new locations and their impact on network coverage. Path loss
models aid in this task, but evaluating and comparing different models requires
a sufficiently large set of high-quality received packet power samples. In this
paper, we report on a corresponding large-scale measurement study covering an
urban area of 200km2 over a period of 230 days using sensors deployed on
garbage trucks, resulting in more than 112 thousand high-quality samples for
received packet power. Using this data, we compare eleven previously proposed
path loss models and additionally provide new coefficients for the Log-distance
model. Our results reveal that the Log-distance model and other well-known
empirical models such as Okumura or Winner+ provide reasonable estimations in
an urban environment, and terrain based models such as ITM or ITWOM have no
advantages. In addition, we derive estimations for the needed sample size in
similar measurement campaigns. To stimulate further research in this direction,
we make all our data publicly available.
