Fast Transmission of Massive Concurrent Alarm Messages in LoRaWAN - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Fast Transmission of Massive Concurrent Alarm Messages in LoRaWAN

Résumé

In the context of the Factories at Major Accident Risk (FMAR), we consider a scenario consisting of multiple sensor nodes that detect dangerous conditions and raise alarms over a LoRaWAN network. Upon event detection, a large number of nodes try to transmit concurrent alarm messages and the reception of at least one message is sufficient for the central server to react. We propose a scheme in which nodes operate in a slotted-time mode after the detection of the triggering event and they can only transmit one packet in a randomly chosen slot. The choice of the transmission slot follows a specific probability distribution that maximizes the probability of successful reception of at least one packet subject to real-time latency constraints. We provide an optimization framework to find the optimal distribution for choosing a transmission slot. We validate the proposed scheme by simulations and provide numerical results to compare the performance for three different slot choice distributions: i) uniform, ii) the distribution used in the Sift protocol [8], and iii) the proposed optimal distribution. The results show that the proposed optimal distribution leads to much better probability of successful packet delivery than other distributions.
Fichier principal
Vignette du fichier
3616388.3617542.pdf (1007.98 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : Copyright (Tous droits réservés)

Dates et versions

hal-04266879 , version 1 (31-10-2023)

Licence

Copyright (Tous droits réservés)

Identifiants

Citer

Dinesh Tamang, Martin Heusse, Andrea Abrardo, Andrzej Duda. Fast Transmission of Massive Concurrent Alarm Messages in LoRaWAN. MSWiM '23: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, Oct 2023, Montréal, Canada. pp.253-257, ⟨10.1145/3616388.3617542⟩. ⟨hal-04266879⟩
335 Consultations
20 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More