Aerospace and Electronic Systems - June 2019 - 50
Prospects of Distributed Wireless Sensor Networks for Urban Environmental Monitoring
Figure 7.
Dislocation of traffic sensors over the Avellino urban area (the white circles with the car symbol inside) and environmental sensors (above
the square with the arrow inside).
heterogeneity is represented in Figure 8, where an UAV
performs a mission flying over a PV field to collect optical
and thermal images of solar panels. Data gathered by the
UAV are processed through a computer vision algorithm
running on-board the vehicle in tight synergy with a geosoftware module, capable of tagging thermographic
images using fixed centimeter-level positions provided by
the commercial U-blox M8 series receivers. The accurate
positioning provided by GNSS signals enables the automation of the entire process allowing to correctly georeference the defective panel inspected by the thermal
camera on-board the UAV. Finally, this information is
provided to the remote service center in charge of the
defect identification and PV plant management.
We refer to the overall architecture as Easy-PV Remotely
Piloted Aircraft System (RPAS) Architecture (see Figure 8)
that is part of the ongoing activities performed in the framework of the EASY-PV project sanctioned under H2020-Galileo-2015-1 call. Please refer to [37], [38] for more details.
CONCLUSION
In this survey, we have highlighted how miniaturization and
low-power design of sensors are pivotal for both fixed and
mobile architecture. Mobile nodes, especially on board of
UAVs, can operate cooperatively with fixed networks as well
as with satellite constellations, thus, paving the ways to new
horizons in urban and rural pervasive monitoring. The consequent challenges of data merging are thus getting harder, making the solution an even more multidisciplinary effort.
Expertise in electronics including sensors, low-power circuits,
and mesh radio transmission, must be combined with software
tools as well as with application and aerospace experts.
REFERENCES
[1] M. Carminati, G. Ferrari, R. Grassetti, and M. Sampietro,
"Real-time data fusion and MEMS sensors fault detection in
an aircraft emergency attitude unit based on Kalman filtering,"
IEEE Sensors J., vol. 12, no. 10, pp. 2984-2992, Oct. 2012.
[2] M. Carminati, M. Rubagotti, R. Grassetti, G. Ferrari, and
M. Sampietro, "A smart embedded control unit for electro-hydraulic aircraft actuators," in Proc. IEEE Elect. Syst.
Aircraft, Railway Ship Propul., 2010, pp. 1-6.
Figure 8.
Easy- PV RPAS architecture model [37], [38].
50
IEEE A&E SYSTEMS MAGAZINE
JUNE 2019
Aerospace and Electronic Systems - June 2019
Table of Contents for the Digital Edition of Aerospace and Electronic Systems - June 2019
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