Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/87237

TítuloAn efficient adaptive data-link-layer architecture for LoRa networks
Autor(es)Coutinho, Micael Bruno Silva
Afonso, José A.
Lopes, Sérgio F.
Palavras-chaveLoRa
Data link layer
Spreading factor
Transmission power
MAC protocol
Data17-Ago-2023
EditoraMultidisciplinary Digital Publishing Institute (MDPI)
RevistaFuture Internet
CitaçãoCoutinho, M.; Afonso, J.A.; Lopes, S.F. An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks. Future Internet 2023, 15, 273. https://doi.org/10.3390/fi15080273
Resumo(s)LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.
TipoArtigo
URIhttps://hdl.handle.net/1822/87237
DOI10.3390/fi15080273
e-ISSN1999-5903
Versão da editorahttps://www.mdpi.com/1999-5903/15/8/273
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:BUM - MDPI

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