Aerospace and Electronic Systems - June 2019 - 49

Carminati et al.

Figure 5.
Clustering topologies for energy-aware radio infrastructure [4].

ongoing NETCHIP Project [6], [7]. Here, we want to highlight an application case of control of environmental parameters and traffic for better management of urban mobility in
order to reduce emissions and improve the livability of people in terms of mental and physical health, lung diseases,
and stress. The idea refers to a mobility system included in
the flexible transit systems, also called Paratransit [36],
something in the middle between the flexibility of private
cars and the fixed scheduled lines of buses.
The proposed framework combines the main benefits
and features of taxi services, web-based booking services
and jitneys (i.e., vehicles of reduced dimensions). Its functioning relies on the collection of several data from many
sources and on data analysis methods in order to retrieve
information on environment, user requirements, and vehicle paths. The architecture will work on implementing
machine learning algorithms and other techniques in real
time (see Figure 6). The main challenges are related to the
transportation modeling, that must be able to well represent the mobility situation inside the area of interest; to
sensor requirements, that must be compliant with the
objectives as previously described; to telecommunications
technique to transmit collected data from the sensors to
the unit of processing; and to the mathematical tools necessary for analyzing the data and the models.
For the scope of this paper, major attention has been given
to sensors and measurements and to sensor networks and
wireless sensor networks. Different sensors are necessary to
collect different data types (data from the territory, weather
data, integrated systems of existing mobility) and different
sensor networks, fixed and wireless, have been designed to
collect and transmit data from the sensors to the processing
center, from the users to the data center, and vice versa.
As a case study, the Avellino city has been monitored
after the setup of the connection network, where traffic and
environment sensors have been placed as illustrated in
Figure 7. The first were realized through STS (Security
Technology Solutions) cameras and the latter with multiparameter ETL 3000 stations. One of the main objectives of
the data analysis was the discovery of possible correlations
between the available data. At this end, specific machine
JUNE 2019

Figure 6.
Functional framework of NETCHIP system [5], [6].

learning algorithms must be designed and developed to
quickly and efficiently extract value information in real time.
For instance, when the correlation between traffic conditions
and weather conditions, such as between pollutant concentrations and weather conditions, are defined, predictive models
can be developed and they can moreover even predict future
conditions of traffic and pollutants, based on weather forecasting. Interested readers may refer to [5] for further details.
Another example of an application case is related to
the monitoring of photovoltaic (PV) plants to assure their
correct functioning. Also in this case, the sensor network
plays a major role in realizing the required control for the
PV maintenance and performance compliance.
Growing attention on climate issues has caused the
worldwide increasing of PV plants production and installation and the consequent promotion of clean energy policies with large amounts of incentives and funding made
available in the specific sector by Governments and the
European Economic Community itself.
Efficient maintenance procedures of PV plants have
become crucial. Recognizing degradation and defects of PV
cells is a very important issue to allow immediate intervention and substitution of modules to avoid output power
losses and performance degradation. The correct and full
functioning of PV plants guarantees the required production
of "clean energy" and therefore it is of crucial importance.
Extended research has been conducted on automated
remotely controlled systems for PV fields inspection, realized through the employment of UAVs to substitute
human intervention on the PVs and to reduce human activity of data collection and some interesting papers have
been published with recent results [37], [38].
A system made of ground stations and UAVs, detecting defects in PV plants, is presented and discussed. The

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Aerospace and Electronic Systems - June 2019

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