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DyLoRa: Towards Energy Efficient Dynamic LoRa Transmission Control

机译:DyLoRa:实现节能的动态LoRa传输控制

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LoRa has been shown as a promising platform for connecting large scale of Internet of Things (IoT) devices, by providing low-power long-range communication with a low data rate. LoRa has different transmission parameters (e.g., transmission power and spreading factor) to tradeoff noise resilience, transmission range and energy consumption for different environments. Thus, adjusting those parameters is essential for LoRa performance. Existing approaches are mainly threshold based and fail to achieve optimal energy efficiency. We propose DyLoRa, a dynamic LoRa transmission control system to improve energy efficiency. The high level idea of DyLoRa is to adjust parameters to different environments. The main challenge is that LoRa has very limited data rate and sparse data, making it very time- and energy-consuming to obtain physical link properties. We show that symbol error rate is highly related to the Signal- Noise Ratio (SNR) and derive the model to characterize this. We further derive energy efficiency model based on the symbol error model. DyLoRa can adjust parameters for optimal energy efficiency from sparse LoRa packets. We implement DyLoRa based on LoRaWAN 1.0.2 with SX1276 LoRa node and SX1301 LoRa gateway and evaluate its performance in real networks. The evaluation results show that DyLoRa improves the energy efficiency by 41.2% on average compared with the state-of-the- art LoRaWAN ADR.
机译:通过提供低数据率的低功率远程通信,LoRa已被证明是连接大型物联网(IoT)设备的有前途的平台。 LoRa具有不同的传输参数(例如,传输功率和扩展因子),以权衡不同环境的噪声弹性,传输范围和能耗。因此,调整这些参数对于LoRa性能至关重要。现有的方法主要是基于阈值的,并且无法实现最佳的能源效率。我们建议使用DyLoRa,这是一种动态的LoRa传动控制系统,可以提高能源效率。 DyLoRa的高级想法是根据不同的环境调整参数。主要挑战在于,LoRa的数据速率和稀疏数据非常有限,因此获取物理链路属性非常耗时且耗能。我们证明了符号错误率与信噪比(SNR)高度相关,并推导了模型来对此进行表征。我们进一步根据符号误差模型推导能效模型。 DyLoRa可以调整参数,以从稀疏的LoRa数据包中获得最佳的能源效率。我们使用SX1276 LoRa节点和SX1301 LoRa网关基于LoRaWAN 1.0.2实现DyLoRa,并评估其在实际网络中的性能。评估结果表明,与最新的LoRaWAN ADR相比,DyLoRa的能源效率平均提高了41.2%。

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