Low power long-range networks like LoRa have become increasingly mainstream for Internet of Things deployments. Given the versatility of applications that these protocols enable, they support many data rates and bandwidths. Yet, for a given network that supports hundreds of devices over multiple miles, the network operator typically needs to specify the same configuration or among a small subset of configurations for all the client devices to communicate with the gateway. This one-size-fits-all approach is highly inefficient in large networks. We propose an alternative approach -- we allow network devices to transmit at any data rate they choose. The gateway uses the first few symbols in the preamble to classify the correct data rate, switches its configuration, and then decodes the data. Our design leverages the inherent asymmetry in outdoor IoT deployments where the clients are power-starved and resource-constrained, but the gateway is not. Our gateway design, Proteus, runs a neural network architecture and is backward compatible with existing LoRa protocols. Our experiments reveal that Proteus can identify the correct configuration with over 97% accuracy in both indoor and outdoor deployments. Our network architecture leads to a 3.8 to 11 times increase in throughput for our LoRa testbed.
翻译:LoRa 等低功率长距离网络已日益成为Thents部署的互联网主流。 鉴于这些协议所允许的应用的多功能性, 它们支持了许多数据率和带宽。 然而, 对于一个支持数英里以上数百个设备的特定网络来说, 网络操作员通常需要为所有客户设备指定相同的配置或小组配置, 以便与网关进行通信。 在大型网络中, 这种一刀切的方法效率极低。 我们提议了一种替代方法 -- 我们允许网络设备以他们选择的任何数据速度传输。 网关使用序言中的第一个符号对正确的数据率进行分类, 转换其配置, 然后解码数据。 我们的设计利用了户外IOT部署的内在不对称性, 客户在户外的IOT部署中是电源紧缩和资源受限制的, 但门户并非如此。 我们的网关设计, Proteus 运行一个神经网络结构, 并且与现有的 LoRa 协议相容。 我们的实验显示, Proteus 可以识别出室内和户外部署的准确度超过97%的正确配置。 我们的网络结构导致3.8至11倍的测试增加。