PERFORMANCE ENHANCEMENTS OF LORAWAN USING MACHINE LEARNING ON THE EDGE
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Date
2021-05
Authors
Otynshin, Anuar
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
LoRaWAN is becoming the dominant long range protocol for Internet of Things (IoT)
devices. However, LoRaWAN’s performance suffers from a high number of collisions
in saturated LoRa networks. To mitigate the number of collisions that happen due
to the time overlap of transmissions on the same channel, we use an edge machine
learning approach. To do so, Reinforcement learning (RL) is leveraged. RL is a
field of machine learning that aims at maximizing the reward by interacting with
the environment. SARSA is an on-policy RL algorithm that uses previous actions
to update the Q-value. This study aims to improve the performance of congested
LoRa networks by allowing RL-based applications on individual nodes. Specifically, it
explores whether periodic applications driven by SARSA can improve the performance
of the network and adapt the period transmissions of the nodes. In this thesis, two
versions of SARSA have been developed, evaluated, and compared to the baseline
of LoRaWAN. To achieve that, several simulations with different configurations are
performed. The simulations include networks with hundreds of nodes and different
number of maximum retransmissions. The results of the simulations have shown that
networks where SARSA algorithms are used present a better performance compared to
the typical LoRaWAN periodic application in certain examined scenarios. The results
demonstrate that RL-based algorithms can significantly improve the performance of
networks with high load. Nevertheless, there is still room for further improvement and
better understanding of the internal mechanisms of the proposed RL approaches.
Description
Keywords
LoRaWAN, RL, reinforcement learning, Internet of Things, IoT, Research Subject Categories::TECHNOLOGY, Type of access: Open Access
Citation
Otynshin, A. (2021). Performance Enhancements of LoRaWAN Using Machine Learning on the Edge (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan