Deep Learning Based Joint Collision Detection and Spreading Factor
Allocation in LoRaWAN
Abstract
Long-range wide area network (LoRaWAN) is a promising low-power network
standard that allows for longdistance wireless communication with great
power saving. LoRa is based on pure ALOHA protocol for channel access,
which causes collisions for the transmitted packets. The collisions may
occur in two scenarios, namely the intra-spreading factor (intra-SF) and
the inter-spreading factor (inter-SF) interference. Consequently, the
SFs assignment is a very critical task for the network performance. This
paper investigates a smart SFs assignment technique to reduce collisions
probability and improve the network performance. In this work, we
exploit different architectures of artificial neural networks for
detecting collisions and selecting the optimal SF. The results show that
the investigated technique achieves a higher prediction accuracy than
traditional machine learning algorithms and enhances the energy
consumption of the network.