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<dc:title>Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings</dc:title>
<dc:creator>Casado Vara, Roberto</dc:creator>
<dc:creator>Vale, Zita</dc:creator>
<dc:creator>Prieto, Javier</dc:creator>
<dc:creator>Corchado, Juan M.</dc:creator>
<dc:subject>Control system</dc:subject>
<dc:subject>Fault-Tolerant control</dc:subject>
<dc:subject>Algorithm desing and analysis</dc:subject>
<dc:subject>IoT (Internet of Things)</dc:subject>
<dc:subject>Nonlinear control</dc:subject>
<dc:description>The monitoring of the Internet of things networks depends to a great extent on the&#xd;
availability and correct functioning of all the network nodes that collect data. This network&#xd;
nodes all of which must correctly satisfy their purpose to ensure the efficiency and high quality&#xd;
of monitoring and control of the internet of things networks. This paper focuses on the problem&#xd;
of fault-tolerant maintenance of a networked environment in the domain of the internet of things.&#xd;
Based on continuous-time Markov chains, together with a cooperative control algorithm, a novel&#xd;
feedback model-based predictive hybrid control algorithm is proposed to improve the maintenance&#xd;
and reliability of the internet of things network. Virtual sensors are substituted for the sensors that&#xd;
the algorithm predicts will not function properly in future time intervals; this allows for maintaining&#xd;
reliable monitoring and control of the internet of things network. In this way, the internet of things&#xd;
network improves its robustness since our fault tolerant control algorithm finds the malfunction nodes&#xd;
that are collecting incorrect data and self-correct this issue replacing malfunctioning sensors with&#xd;
new ones. In addition, the proposed model is capable of optimising sensor positioning. As a result,&#xd;
data collection from the environment can be kept stable. The developed continuous-time control&#xd;
model is applied to guarantee reliable monitoring and control of temperature in a smart supermarket.&#xd;
Finally, the efficiency of the presented approach is verified with the results obtained in the conducted&#xd;
case study.</dc:description>
<dc:date>2023-01-18T08:00:09Z</dc:date>
<dc:date>2023-01-18T08:00:09Z</dc:date>
<dc:date>2018-12</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:identifier>http://hdl.handle.net/10259/7262</dc:identifier>
<dc:identifier>10.3390/en11123430</dc:identifier>
<dc:identifier>1996-1073</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Energies. 2018, V. 11, n. 12, e3430</dc:relation>
<dc:relation>https://doi.org/10.3390/en11123430</dc:relation>
<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>Atribución 4.0 Internacional</dc:rights>
<dc:publisher>MDPI</dc:publisher>
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