Please use this identifier to cite or link to this item: https://repository.rsif-paset.org/xmlui/handle/123456789/411
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dc.contributor.authorFall, Ndeye Penda-
dc.contributor.authorMarot, Michel-
dc.contributor.authorDiallo, Cherif-
dc.contributor.authorBernard, Antoine-
dc.contributor.authorRoujanski, Gatien-
dc.date.accessioned2024-07-07T10:30:23Z-
dc.date.available2024-07-07T10:30:23Z-
dc.date.issued2024-06-26-
dc.identifier.urihttps://repository.rsif-paset.org/xmlui/handle/123456789/411-
dc.descriptionPublished in: GLOBECOM 2023 - 2023 IEEE Global Communications Conference https://doi.org/10.1109/GLOBECOM54140.2023.10437583en_US
dc.description.abstractThe evolution of vehicular networks continues with the advent of LPWAN (Low Power Wide Area Network). Thus, several studies currently concern the integration of LPWANs with ITS (Intelligent Transportation Systems). Among these LPWANs, LoRa with LoRaWAN, with its coverage capabilities, support of mobility, and implementation cost, is in the spotlight. However, the latter presents performance problems in highly mobile environments. Therefore, optimizing mobility in LoRaWAN is imperative for its integration into ITS. This requires performance improvements such as loss containment in very dense environments. In this paper, a resource reservation approach is proposed. It is based on device trajectory prediction and traffic differentiation in a dense multi-operator environment. This mechanism aims at reserving resources before the arrival of the device on the predicted antenna, thus reducing the rejections at the join phase while favoring “its subscribers”.en_US
dc.publisherIEEE Xploreen_US
dc.subjectLoRaWAN , LoRa , resource reservation , service class , vehicularen_US
dc.titleOptimizing Mobility in LoRaWan: A Resource Reservation Approachen_US
dc.typePresentationen_US
Appears in Collections:ICTs including Big Data and Artificial Intelligence

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