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Review and experimental evaluation of ADR enhancements for LoRaWAN networks

Author

Listed:
  • Norhane Benkahla

    (Carthage University
    Université de Lorraine)

  • Hajer Tounsi

    (Carthage University)

  • Ye-Qiong Song

    (Université de Lorraine)

  • Mounir Frikha

    (Carthage University
    King Faisal University (KFU))

Abstract

With the ever growing internet of things market, Low Power Wide Area Networks (LPWAN) become more and more attractive. Among the various LPWAN technologies, LoRa (and LoRaWAN) has drawn a lot of research attention with its great adaptation capability. In fact, its spectrum modulation spreading factor, transmission power, data rate, channel bandwidth, and coding rate are so many configurable parameters allowing to cope with a large number of use cases. In addition, through its ADR (Adaptive Data Rate) function, LoRa offers an interesting mechanism to self-adapt its configuration to its operational conditions. Several works have been carried out for extending ADR to further improve its performance for different use cases. LoRa has even been recently investigated on its capacity to support the mobility, although it is not originally designed for. In this paper, we first review the most significant ADR enhancements proposed in the literature, then describe our earlier E-ADR proposal [1] aiming at dealing with the node mobility use case. This paper also provides experimental performance evaluations of the reviewed ADR enhancements in a mobile node scenario, highlighting the unique feature of E-ADR.

Suggested Citation

  • Norhane Benkahla & Hajer Tounsi & Ye-Qiong Song & Mounir Frikha, 2021. "Review and experimental evaluation of ADR enhancements for LoRaWAN networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 1-22, May.
    • Handle: RePEc:spr:telsys:v:77:y:2021:i:1:d:10.1007_s11235-020-00738-x
    DOI: 10.1007/s11235-020-00738-x
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    Cited by:

    1. Honggang Wang & Peidong Pei & Ruoyu Pan & Kai Wu & Yu Zhang & Jinchao Xiao & Jingfeng Yang, 2022. "A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway," Mathematics, MDPI, vol. 10(21), pages 1-21, October.

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