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Multi-agent LoRaWAN Network for End-of-Life Management of Electric Vehicle Batteries

  • Celia Garrido-Hidalgo [1] ; Luis Roda-Sanchez [1] ; Teresa Olivares [1] Árbol académico ; F. Javier Ramírez [1] ; Antonio Fernández-Caballero [1] Árbol académico
    1. [1] Universidad de Castilla-La Mancha

      Universidad de Castilla-La Mancha

      Ciudad Real, España

  • Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.) Árbol académico, José Ramón Álvarez Sánchez (dir. congr.) Árbol académico, Félix de la Paz López (dir. congr.) Árbol académico, Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 505-514
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • The LoRaWAN standard has become one of the most extended Internet-of-Things technologies in both academia and industry due to its long communication range and high energy efficiency. Given the fast-growth expectations of the reverse logistics sector —partly caused by an imminent number of electric-vehicle batteries to be disposed of in the coming years—, the adoption of wireless machine-type communications promises several benefits towards products’ end-of-life monitoring and diagnosis. While LoRaWAN seems a suitable technology for this purpose, its scalability limitations need to be first resolved. To shed light on this matter, this work presents a multi-agent approach to support an efficient allocation of network resources in time-slotted communications running on top of LoRaWAN’s MAC layer. By considering different slot-length computation strategies, the multi-agent network components interact with joining LoRaWAN devices and assign them to the most convenient transmission schedule which, in turn, depends on both application and hardware-specific constraints. Our results point to network scalability improvements ranging from 43.22% to 86.54% depending on the slot-length computation strategy being applied


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