2026-06-02T04:00:44Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72622023-01-19T01:05:17Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

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