Improving Delivery Ratio in LoRa Network

Rafał Marjasz, Konrad Polys, Anna Strzoda, K. Grochla
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引用次数: 3

Abstract

The LoRa (Long Range) radio communication provides large coverage areas, low bandwidth, small data rates, and transmitting limited data sizes, with very low energy usage. Multiple transmission channels are used, but the number of channels available is fairly limited - e.g. 8 in most countries in Europe. The amount of interferences may be estimated by the continuous observation of the number of delivered packets and the received signal strength. We propose to aggregate and analyse those metrics per channel and geographical area. When the proportion of received packets per some of the areas decreases, it is subjected to heavier interferences than other parts of the network and should be used less intensively. Thus we propose to allocate the weights for each channel per geographic area and/or device, defining the probability of the channel usage. Furthermore, we created an algorithm that periodically updates the weights and a simulation model using OMNeT++, FloRa, taking into account possible packets collisions. The algorithm has been validated for both random and realistic network topologies. The simulation evaluations show a significant improvement in the package delivery efficiency, by almost 50% in scenarios with very high interference level simulated and by more than 13% in scenarios fed with real-life measurements of packet delivery probability changes in time.
提高LoRa网络的交付率
LoRa (Long Range)无线通信具有覆盖面积大、带宽低、数据速率小、传输数据量有限、能耗极低等特点。使用多个传输通道,但可用的通道数量相当有限,例如在大多数欧洲国家只有8个。干扰的数量可以通过连续观察发送的包的数量和接收的信号强度来估计。我们建议汇总和分析每个渠道和地理区域的这些指标。当某一区域接收数据包的比例降低时,该区域受到的干扰比网络的其他部分更大,应减少密集使用。因此,我们建议为每个地理区域和/或设备分配每个信道的权重,定义信道使用的概率。此外,考虑到可能发生的数据包冲突,我们创建了一个定期更新权重的算法和一个使用omnet++, FloRa的仿真模型。该算法在随机网络拓扑和现实网络拓扑下都得到了验证。模拟评估显示,包裹递送效率显著提高,在模拟干扰水平非常高的情况下提高了近50%,在实时测量数据包递送概率随时间变化的情况下提高了13%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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