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oai:riubu.ubu.es:10259/72542023-01-18T01:05:25Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

2023-01-17T11:48:20Z urn:hdl:10259/7254 Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka–Volterra Ecosystem Model Casado Vara, Roberto Canal-Alonso, Ángel Martín del Rey, Ángel Prieta, Fernando de la Prieto, Javier Internet of Things Lotka-Volterra model Predator-prey system Bio-inspired system evolution Algorithm desing Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka–Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka–Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka–Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT. 2023-01-17T11:48:20Z 2023-01-17T11:48:20Z 2019-10 info:eu-repo/semantics/article http://hdl.handle.net/10259/7254 10.3390/s19214642 1424-8220 eng Sensors. 2019, V. 19, n. 21, e4642 https://doi.org/10.3390/s19214642 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI