A Systematic Literature Review on Machine Learning Methods for Improving LoRaWAN Performance
DOI:
https://doi.org/10.37134/ejoss.vol12.sp2.21.2026Keywords:
LoRaWAN, Machine Learning, Systematic Literature Review, Edge-AI, Network Optimization, Internet of Things (IoT)Abstract
The growing deployment of the Internet of Things (IoT) devices makes the existing LoRaWAN networks increasingly prone to performance degradation under crowded networking conditions. To ensure sustainable development of LoRaWAN technology, new strategies to optimize power consumption, enhance network scalability, and alleviate congestion need to be designed. The integration of Machine Learning (ML) technologies into LoRaWAN architecture is expected to be a key solution to addressing critical infrastructure challenges. The objective of this study is to conduct a systematic literature review of the evolution of ML-enhanced LoRaWAN networks following to PRISMA 2020 guidelines. This review paper discusses four research questions: ML approaches applied, KPIs addressed, real-world applications of ML-based LoRaWAN networks, and knowledge gaps in the area. Analysis is conducted based on 70 scholarly papers published between 2020 and 2025. The review identifies prevalent ML approaches, such as supervised learning, unsupervised learning, and reinforcement learning, and explores their influence on various KPIs, including data rate, latency, PDR, and others. Instead of relying on the cloud, ML applications in LoraWAN networks are moving toward edge computing. Minimizing backhaul traffic resulting from ML algorithms is run at the gateway and end-device levels. Precision agriculture and smart urban infrastructure are where ML algorithms significantly enhance monitoring capabilities. Access to standardized datasets remains limited, making validation of ML algorithms a challenge at the same time. The strategic framework proposed for future research is moving away from centralized, cloud-centric processing toward intelligent, hybrid edge-cloud architectures.
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