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Recherche transversale
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2018-05-14 - Article/Dans un journal avec peer-review - Anglais - 12 page(s)

Debauche Olivier , Mahmoudi Said , Andriamandroso Andriamasinoro Lalaina Herinaina, Manneback Pierre , Bindelle Jérôme, Lebeau Frédéric, "Cloud Services Integration For Farm Animals’ Behavior Studies Based On Smartphones As Activity Sensors" in Journal of Ambient Intelligence and Humanized Computing, 10.1007/s12652-018-0845-9

  • Edition : Springer, Berlin (Germany)
  • Codes CREF : Modèles mathématiques d'aide à la décision (DI1151), Statistique mathématique (DI1130), Statistique appliquée (DI1133)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) Smartphones, particularly iPhone, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are equipped with high performance Inertial Measurement Units (IMU) and absolute positioning systems analyzing users’ movements, but they can easily be diverted to analyze likewise the behaviors of domestic animals such as cattle. The study of animal behavior using smartphones requires the storage of many high frequency variables from a large number of individuals and their processing through various relevant variables combinations for modeling and decision-making. Transferring, storing, treating and sharing such an amount of data is a big challenge. In this paper, a lambda cloud architecture innovatively coupled to a scientific sharing platform used to archive, and process high-frequency data are proposed to integrate future developments of the Internet of Things applied to the monitoring of domestic animals. An application to the study of cattle behavior on pasture based on the data recorded with the IMU of iPhone 4s is exemplified. Performances comparison between iPhone 4s and iPhone 5s is also achieved. The package comes also with a web interface to encode the actual behavior observed on videos and to synchronize observations with the sensor signals. Finally, the use of Edge computing on the iPhone reduced by 43.5% on average the size of the raw data by eliminating redundancies. The limitation of the number of digits on individual variable can reduce data redundancy up to 98.5%.

Mots-clés :
  • (Anglais) IMU
  • (Anglais) lambda architecture
  • (Anglais) smart agriculture
  • (Anglais) iPhone
  • (Anglais) animals' behavior
  • (Anglais) precision livestock farming