The number of IoT devices is predicted to reach 125 billion by 2023. The
growth of IoT devices will intensify the collisions between devices, degrading
communication performance. Selecting appropriate transmission parameters, such
as channel and spreading factor (SF), can effectively reduce the collisions
between long-range (LoRa) devices. However, most of the schemes proposed in the
current literature are not easy to implement on an IoT device with limited
computational complexity and memory. To solve this issue, we propose a
lightweight transmission-parameter selection scheme, i.e., a joint channel and
SF selection scheme using reinforcement learning for low-power wide area
networking (LoRaWAN). In the proposed scheme, appropriate transmission
parameters can be selected by simple four arithmetic operations using only
Acknowledge (ACK) information. Additionally, we theoretically analyze the
computational complexity and memory requirement of our proposed scheme, which
verified that our proposed scheme could select transmission parameters with
extremely low computational complexity and memory requirement. Moreover, a
large number of experiments were implemented on the LoRa devices in the real
world to evaluate the effectiveness of our proposed scheme. The experimental
results demonstrate the following main phenomena. (1) Compared to other
lightweight transmission-parameter selection schemes, collisions between LoRa
devices can be efficiently avoided by our proposed scheme in LoRaWAN
irrespective of changes in the available channels. (2) The frame success rate
(FSR) can be improved by selecting access channels and using SFs as opposed to
only selecting access channels. (3) Since interference exists between adjacent
channels, FSR and fairness can be improved by increasing the interval of
adjacent available channels.Comment: 14 pages, 12 figures, 8 tables. This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl