  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.
