Instrumentation & Measurement Magazine 23-9 - 34

Fig. 4. Response Surface Reconstructed for CR=1.

Fig. 5. Response Surface Reconstructed for CR=4.

Fig. 6. Response Surface Reconstructed for CR=1 without external lateral
face.

Fig. 7. Response Surface Reconstructed for CR=4 without external lateral
face.
34	

Moreover, it can be observed that by increasing the CR
(runs 7 and 8), while other parameters are unchanged, a
less significant improvement of the packet loss is obtained
compared to improvement that it is possible to achieve by increasing the SF or reducing the bandwidth. The lost packets
decrease from 11.5% to 6.8%, when CR increases from 1 to 4.
In Table 5, the experimental results obtained for each factorial point design of the CCD analysis are shown.
Runs from 9 to 16 highlight the robustness of LoRa wireless
technology versus white gaussian noise when high levels of SF
are selected at PHY layer. High level of SF (>=10) allows to obtain the best performance, in fact, also with a large bandwidth,
or low CR, lost packets rate is close to zero.
In Table 6, the experimental results obtained for each center
point design of the CCD analysis are shown.
For the central point returned from the CCD analysis
twelve repeated measurements have been performed. As a
result, an average rate of lost packets equal to 16.3% and a standard deviation of 2.3% has been obtained.
Finally, by applying a regression linear technique the
model has been identified and the whole response surface
reconstructed. In Fig. 4 and Fig. 5 the response surface reconstructed for CR=1 and for CR=4, respectively, are displayed by
means of a 3D graph.
In Fig. 6. and Fig. 7 are instead represented the same graphs
of Fig. 4 and Fig. 5 from a different point of view, with no external lateral face, to better appreciate variations within the
surface response.
To verify the accuracy of the identified model, the experiments were repeated on some points taken at random among
all the possible 625 combinations of the full factorial analysis,
different from those used to carry out the CCD analysis. Once
the measurements have been performed, the PLR values thus
obtained were compared to those returned by the model in
the same points. It was therefore verified that the uncertainty,
given by the difference between PLR measured and PLR returned by the model at the same point, was always less than
the greater uncertainty of the model, proving the feasibility of
the proposed method to reduce the experimental burden of the
LoRa's assessment procedure.

Conclusions
In conclusion, LoRa communication technology is emerging
as a leader among low power wide area networks used for IoT
applications, such as Smart City, Smart Agriculture, etc. This
success is mainly associated with its intrinsic characteristics
and the exploitation of license-free frequency bands. However,
due to the rapid spread of IoT applications operating in the
same frequency range, LoRa communications may suffer from
in-channel coexistence and interference problems. For this reason, it is important to carry out a performance assessment of
IoT devices based on LoRa communication technology in both
design and maintenance stages of the product life cycle. To this
aim, the authors have proposed to use a Cross-Layer approach
to experimentally correlate the values that characterize major PHY layer quantities to those assumed by key higher layer

IEEE Instrumentation & Measurement Magazine	

December 2020



Instrumentation & Measurement Magazine 23-9

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