Instrumentation & Measurement Magazine 23-9 - 35

parameters, thus providing useful information to fix possible problems at the vulnerable PHY layer. In particular, the
authors' attention has been focused on the dependence exhibited by PLR, a well-known key higher layer parameter, on the
values that characterize major PHY quantities, such as different configurations of the LoRa signal and intentional AWGN
power levels. However, since a comprehensive characterization requires a full factorial experiment, that could take a lot
of time and become expensive for manufacturers, the authors
propose to carry out the Cross-Layer analysis through a proper
design of experimental technique, namely CCD, instead of the
full factorial experiment, to notably reduce the number of experiments without loss of the useful information and model
accuracy. The feasibility of the proposed approach has been experimentally proved using a suitable measurement procedure.

References
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Pasquale Arpaia (pasquale.arpaia@uniina.it) is Professor of
instrumentation and measurements with the Department of
Electrical Engineering and Information Technology at University of Napoli Federico II, Italy and Scientific Advisor of
the High-Tech FabLab. He is Team Leader at European Organization for Nuclear Research (CERN). His main research
interests include digital instrumentation and measurement
techniques for magnets, superconductors, power converters
and cryogenics of particle accelerators, evolutionary diagnostics, IoT-based measurement systems, ADC modelling and
testing.

meter for the IoT, " in Proc. 2018 IEEE Int. Instrum. Meas. Technol.
Conf. (I2MTC), pp. 1-6, 2018.
[4]	 L. Angrisani, P. Arpaia, F. Bonavolonta and R. Schiano Lo
Moriello, " Academic FabLabs for industry 4.0: experience at
University of Naples Federico II, " IEEE Instrum. Meas. Mag., vol.
21, no. 1, pp. 6-13, Feb. 2018.
[5]	 G. Betta, G. Cerro, M. Ferdinandi, L. Ferrigno and M. Molinara,
" Contaminants detection and classification through a customized
IoT-based platform: a case study, " IEEE Instrum. Meas. Mag., vol.

Francesco Bonavolontà is a Research Fellow in the Department of Electrical and Information Technologies at University
of Naples Federico II, Italy and holds a Ph.D. degree. His research activity is centered in the area of instrumentation
and measurement and is mainly focused on developing of
innovative DAS systems based on compressive sampling techniques, also including Internet of Things devices for smart
applications.

22, no. 6, pp. 35-44, Dec. 2019.
[6]	 L. Angrisani, F. Bonavolontà, A. Liccardo, and R. Schiano Lo
Moriello, " On the use of LoRa technology for logic selectivity in
MV distribution networks, " Energies, vol. 11, no. 3079, 2018.
[7]	 D. Magrin, M. Centenaro, and L. Vangelista, " Performance
evaluation of LoRa networks in a smart city scenario, " in Proc.
2017 IEEE Int. Conf. Communications (ICC), pp. 1-7, 2017.
[8]	 C. Orfanidis, L. M. Feeney, M. Jacobsson, and P. Gunningberg,
" Investigating interference between LoRa and IEEE 802.15.
4G networks, " in Proc. 2017 IEEE Wireless Mobile Computing,

Dominque Dallet is Full Professor in the field of digital electronic design with the Engineering School Bordeaux INP,
France and is the Head of the Electronic Design Group at the
IMS Laboratory, University of Bordeaux, France. His research
interests include data converter (A/D-D/A) modelling and
testing, parameter estimation, digital signal processing implementation, and electronic design for the digital enhancement
of analog and mixed electronic circuits (ADC, DAC, power
amplifiers).

Networking and Commun. (WiMob), pp. 1-8, 2017.
[9]	 D. Capriglione, G. Cerro, L. Ferrigno, and G. Miele, " Effects
of real instrument performance of an energy detection-based
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5, pp. 1302-1312, 2019.
[10]	D. Capriglione, G. Cerro, L. Ferrigno, G. Miele, " Performance
analysis of a two-stage spectrum sensing scheme for dynamic
spectrum access in TV bands, " Measurement, vol. 135, pp. 661-671,
2019.
[11]	D. Capriglione, G. Cerro, L. Ferrigno, G. Miele, " Analysis and
implementation of a wavelet based spectrum sensing method for

December 2020	

Annarita Tedesco is currently pursuing her Ph.D. degree
in physical sciences and engineering at the University of
Bordeaux, France, after having received the M.S. degree in telecommunication engineering in 2002. For many years she was
involved in research and development divisions of world wide
technology companies, promoting strong cooperation with
the academic world. Her research interests focus on industrial
measurement, monitoring systems, AR-based measurement
systems, measurements for Industry 4.0, sensor networks, human computer interaction, and brain computer interface.

IEEE Instrumentation & Measurement Magazine	35
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