A neural network propagation model for LoRaWAN and critical analysis with real-world measurements

Salaheddin Hosseinzadeh, Mahmood Almoathen, Hadi Larijani, Krystyna Curtis

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    17 Citations (Scopus)
    147 Downloads (Pure)


    Among the many technologies competing for the Internet of Things (IoT), one of the most
    promising and fast-growing technologies in this landscape is the Low-PowerWide-Area Network
    (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been
    studied for outdoor environments. However, this article focuses on end-to-end propagation in
    an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor
    metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning
    and coverage prediction, a novel hybrid propagation estimation method has been developed and
    examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized
    Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared
    against different propagation models. For benchmarking, log-distance and COST231 models were
    used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa
    Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment
    compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate
    estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be
    addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward
    neural network combined with a COST231 model improves the accuracy of the predictions. This
    work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in
    similar scenarios. This could facilitate network planning for outdoor–indoor environments.
    Original languageEnglish
    Number of pages10
    JournalBig Data and Cognitive Computing
    Issue number1
    Publication statusPublished - 14 Dec 2017


    • COST231 multi-wall model
    • Feedforward neural networks
    • LPWAN
    • LoRaWAN
    • Propagation analysis and modeling


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