K-Means Spreading Factor Allocation for Large-Scale LoRa Networks.

Muhammad Asad Ullah,Arliones Hoeller,Hirley Alves,Richard Souza,Junnaid Iqbal

doi:10.3390/s19214723

Muhammad Asad Ullah, Arliones Hoeller + Show 3 more

Open Access

https://doi.org/10.3390/s19214723

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Abstract

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by times.

Highlights

The Internet of Things (IoT) is the integration of modern electronic devices, smart sensors, internet protocols, and wireless communications technologies
We are sacrificing network quality for lower spreading factor (SF) with fewer nodes and high success probabilities, as presented in Figure 7, we improved the performance of the network for the higher SFs and regions with more nodes, where the network performance was weak in the baseline model from [19], which considers fixed distance steps from the gateway to define the SF allocation
This paper has presented a novel SF allocation technique for a large-scale LoRa network using the

Summary

Introduction

The Internet of Things (IoT) is the integration of modern electronic devices, smart sensors, internet protocols, and wireless communications technologies. According to a Gartner Inc. report, there will be around 26 billion IoT devices deployed worldwide by 2020 [6]. In the Statista report, it is predicted that there will be over billion IoT devices worldwide by 2025 [7]. The spectacular growth and transformation of wireless connectivity are driven by the IoT paradigm, with technologies having attributes of large-scale network infrastructure with low-cost sensors connected to the Internet. In this context, low-power wide-area networks (LPWANs) are quite popular in terms of prototypes, standards, and on the commercial level because of their

Results

Discussion

Conclusion