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2020-11-12 - Article/Dans un journal avec peer-review - Anglais - 8 page(s)

El Moulat Meryem, Debauche Olivier , Mahmoudi Said , Mahmoudi Sidi , Manneback Pierre , Lebeau Frédéric, "Edge Computing and Artificial Intelligence for Landslides Monitoring" in Procedia Computer Science, 177, 480-487

  • Edition : Elsevier, Amsterdam (Netherlands)
  • Codes CREF : Sciences agronomiques (DI3600), Informatique générale (DI1162)
  • 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) Landslides are phenomena widely present around the world and responsible each year of numerous life loss and extensive property damage. Researchers have developed various methodologies to identify area of high susceptibility of landslides. However, these methodologies cannot predict ‘when’ landslides are going to take place. Indeed, Wireless Sensors Network (WSN), Internet of Things (IoT) and Artificial Intelligence (AI) offer the possibility to monitor in real-time parameters causing the triggering factors of rapid landslides. In this paper, we suggest a real-time monitoring of landslides in order to precociously alert population in dangerous situation by means of a warning system. The novelty of this paper is the coupling of wireless sensors network and a multi-agent system deployed on an edge AI-IoT architecture by means of Kubernetes and Docker.

Identifiants :
  • DOI : 10.1016/j.procs.2020.10.066

Mots-clés :
  • (Anglais) early warning system
  • (Anglais) Landslides susceptibility
  • (Anglais) landslides monitoring
  • (Anglais) Internet of Things
  • (Anglais) Artificial Intelligence
   
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