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Communication Dans Un Congrès Année : 2023

Impacts of GPS module on energy consumption and machine-learning based battery lifetime estimation

Résumé

The maintenance of a Wireless Sensor Network may represent a logistic challenge. Battery replacement on applications involving huge numbers of sensors spread over a wide and distant area may be expensive and difficult. So, good planning of the maintenance schedule is necessary. This work discusses the impact of the GPS module for bovine tracking on farms on current consumption and its estimation. It compares Long-Short Term Memory (LSTM) networks and Decision Trees with AdaBoost to estimate this consumption. The results show that the activation of the GPS module increases 114.36% the current consumption and Decision Trees with AdaBoost using 300 estimators and with a depth equal to 20 outperforms LTSM with Root Mean Square (RMS) error of 0.00015.
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Dates et versions

hal-04077301 , version 1 (21-04-2023)

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André Teixeira de Aquino, José Ailton Leão Barboza Júnior, Nicolas de Araujo Moreira, Paulo Peixoto Praça. Impacts of GPS module on energy consumption and machine-learning based battery lifetime estimation. 10th International Workshop on ADVANCEs in ICT Infrastructures and Services(ADVANCE 2023), Federal University of Ceara, University of Evry, Feb 2023, Fortaleza-Jerricoacoara, Brazil. 11p, ⟨10.48545/advance2023-fullpapers-3_3⟩. ⟨hal-04077301⟩

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