A Systematic Literature Review on Machine Learning Algorithms for LoRaWAN Network Optimization: Current Trends and Future Directions

DOI

10.2991/978-94-6239-745-3_13How to use a DOI?

Keywords

Internet of Things; LoRaWAN; Machine Learning; Network Optimization; Systematic Literature Review

Abstract

The long-range communication and energy efficiency of LoRaWAN have enabled it to be a significant Low Power Wide Area Network (LPWAN) technology in the fast-growing Internet of Things (IoT). Nevertheless, Lo-RaWAN faces very severe technological challenges as the density of the network increases, i.e., loss of packets and limited scalability in large-scale implementation cases. Traditional network management methods, such as Adaptive Data Rate (ADR), are often not reliable in dynamic and uncertain environments. As a result, there is a growing trend toward incorporating Machine Learning (ML) to improve network performance via intelligent adaptation. The purpose of this Systematic Literature Review (SLR) is to determine and examine the most popular algorithmic trends for LoRaWAN optimization. The results offer a thorough classification of existing intelligent solutions and point out unresolved research issues, acting as a guide for creating LoRaWAN frameworks that are more robust and self-optimizing in the context of Industry 4.0.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Hanis Shazana Mohamed Soid
AU  - Mad Helmi Ab. Majid
AU  - Abu Bakar Ibrahim
PY  - 2026
DA  - 2026/08/24
TI  - A Systematic Literature Review on Machine Learning Algorithms for LoRaWAN Network Optimization: Current Trends and Future Directions
BT  - Proceedings of the International Conference on Research, Innovation, Sustainability, and Educations (IRISECON 2026)
PB  - Atlantis Press
SP  - 172
EP  - 207
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6239-745-3_13
DO  - 10.2991/978-94-6239-745-3_13
ID  - Soid2026
ER  -