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<journal-meta>
<journal-id journal-id-type="publisher">ICA-Proc</journal-id>
<journal-title-group>
<journal-title>Proceedings of the ICA</journal-title>
<abbrev-journal-title abbrev-type="publisher">ICA-Proc</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Proc. Int. Cartogr. Assoc.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2570-2092</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/ica-proc-4-25-2021</article-id>
<title-group>
<article-title>Remote sensing based on time variance control in configurable area partitioning</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>De Falco</surname>
<given-names>Stefano</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fiorentino</surname>
<given-names>Giulia</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Dept. of Political Sciences, University Federico II of Naples, Italy</addr-line>
</aff>
<pub-date pub-type="epub">
<day>03</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>4</volume>
<elocation-id>25</elocation-id>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2021 Stefano De Falco</copyright-statement>
<copyright-year>2021</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
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<self-uri xlink:href="https://ica-proc.copernicus.org/articles/4/25/2021/ica-proc-4-25-2021.pdf">The full text article is available as a PDF file from https://ica-proc.copernicus.org/articles/4/25/2021/ica-proc-4-25-2021.pdf</self-uri>
<abstract>
<p>&lt;p&gt;In this paper a sensor data fusion approach for characteristics field monitoring, based on time variance control model, is proposed. Distributed sensing and remote processing are the basic features of the employed architecture. In fact, in order to obtain meaningful information about the temporal and spatial variations, which characterize the field levels of some characteristics (electromagnetic, air pollution, seismic, etc), a distributed network of wireless and mobile smart-sensors has been designed.&lt;/p&gt;&lt;p&gt;Starting from the partitioned configuration of a monitored geographic areas, this model allows to take into account the different levels of degradation over time in the sensors&apos; performances associated with the different geographic partitions, progressively increasing the severity of the control. To this end, through the introduction of a reliability curve, a revised traditional control chart for variables is proposed.&lt;/p&gt;&lt;p&gt;The proposed approach, further constituting an element of the scientific debate, aims to be a useful operational tool for professionals and managers employed in the environment control.&lt;/p&gt;</p>
</abstract>
<counts><page-count count="9"/></counts>
</article-meta>
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