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oai:riuma.uma.es:10630/301472026-02-03T11:05:19Zcom_10630_2254col_10630_37953

Urda, Daniel Cañete, Eduardo Subirats Contreras, José Luis Franco, Leónardo Llopis-Torres, Luis Manuel Jerez-Aragonés, José Manuel 2024-02-08T13:14:32Z 2024-02-08T13:14:32Z 2012-11 Muñoz, D. U., Carmona, E. C., Contreras, J. L. S., Franco, L., Torres, L. M. L., & Aragonés, J. M. J. (2012). Energy-efficient reprogramming in WSN using constructive neural networks. International Journal of Innovative Computing, Information and Control, 8(11), 7561–7578. 1349-4198 https://hdl.handle.net/10630/30147 In this paper, we propose the use of neural based technologies to carry out the dynamic reprogramming of wireless sensor networks as an alternative to traditional methodology. An analysis an comparison of the energy cost involved in reprogramming wireless sensor networks was done using rule-based programming (TP) standard feeforward neural networks (FF), and the C-Mantec (CM) algorithm, a novel method based on constructive neural networks. The simulation results, first performed on array of sensor networks under COOJA simulator (considering best, medium and worst case scenarios for three benchmark problems) and finally evaluated on a case os study with identical conditions, show that the use of neural network based methodoligies (FF & CM) produces a significant saving in resources, measured by the number of packets transmitted, the energy consumed and the time needed to reprogram the sensors. eng open access Redes neuronales (Informática) Energy-efficient reprogramming in wsn using constructive neural networks. journal article