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Lifetime Increase for Wireless Sensor Networks Using Cellular Learning Automata

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Abstract

With the advent of automation, more and more information is being generated. Thereby, increasingly sensors are being used, featuring increasingly dense networks. In the case of sensor nodes, it is necessary to have usage management of them, to have better energy management. The Discrete Event Systems are presented as a solution to a better elaboration of applied logics and harnessing of sensors. And more, through the use of Cellular Learning Automata, there is the possibility of elaborate, intelligent groups that attends the systems requirement of coverage in an optimized way. Therefore, this work presents a model for characterization of a wireless network sensor, based on discrete event systems theory and considering cellular learning automata, to the optimization of energy consumption in a wireless sensor network through the formation of groups at increasing the network’s lifetime. The model was developed, analyzed, and validated using the computational tool Stateflow in MATLAB®/Simulink®. Algorithms corresponding to the models and assemblies were developed to validate the methods. From the results obtained in simulation, was verified a decrease in average consumption of network up to 67.54% and an increase of the network’s lifetime up to 192.86% in scenarios under analysis. The experimental results were performed using a wireless sensor network with five sensors, with an increase in the lifetime of 31.71%.

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Authors and Affiliations

  1. Federal University of Paraíba, Joao Pessoa, Brazil

    Rafael Pereira de Medeiros, Juan Moises Mauricio Villanueva & Euler Cássio Tavares de Macedo

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  1. Rafael Pereira de Medeiros
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  2. Juan Moises Mauricio Villanueva
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  3. Euler Cássio Tavares de Macedo
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Correspondence to Rafael Pereira de Medeiros.

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de Medeiros, R.P., Villanueva, J.M.M. & de Macedo, E.C.T. Lifetime Increase for Wireless Sensor Networks Using Cellular Learning Automata. Wireless Pers Commun 123, 3413–3432 (2022). https://doi.org/10.1007/s11277-021-09295-1

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  • DOI: https://doi.org/10.1007/s11277-021-09295-1

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