| dc.description.abstract | The LoRa technology stands out for allowing the connection of a large number of devices
allocated several kilometers away from the base station, with low energy consumption
and maintenance costs. To do so, each device has a set of settings that
define how it will work, like carrier frequency, bandwidth, and so on. Choosing the
right configuration is essential for a well working network. In the LoRa technology, the
communication is usually controlled by the LoRaWAN protocol, that defines how the
network will exchange messages. In this protocol, there are instructions that change
some configurations of the connected devices, optimizing its performance by either
enlarging the transmission’s data rate or lowering the energy consumption. However,
this optimization considers just the device itself, and not how it will affect the rest of
the network. This project compares the performance of a network that is configured
by the LoRaWAN protocol with the performance of a network configured by a genetic
algorithm, which is capable of evaluating the network as a whole and search solutions
tending to the optimum. The genetic algorithm was implemented in Python and the
network performance was measured using simulations based on existing works. It was
possible to see that, for a small amount of devices connected on the network, the LoRaWAN protocol has a good enough performance. Still, when the network complexity
gets larger, solutions generated by the genetic algorithm are superior than the ones
generated by the protocol. | |
