A Systematic Mapping Study on Performance and Robustness Optimization of LoRaWAN Networks
TL;DR
This study identifies and prioritizes five research gaps, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices.
Abstract
Long-Range Wide-Area Networks (LoRaWANs) combine long-range and low-power communication, making them a key technology for Internet of Things (IoT) applications. This systematic mapping study provides a comprehensive analysis of research on LoRaWAN network technology, focusing on performance and robustness optimization published between 2015 and 2026. Through a rigorous screening of2746 papers, we identified and analyzed 209 papers that met strict inclusion criteria and addressed network-layer optimization mechanisms. The studies were retrieved from IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus using a PICO-based search strategy, and synthesized descriptively without effect-size meta-analysis. Our analysis reveals a rapidly growing research field, with 53.1% of the 209 included studies were published in the recent period (2023–2026), predominantly simulation-based evaluation approaches (72.2%), and strong geographic concentration in Europe (38.8%) and Asia (35.4%). We identified that performance optimization is the primary focus (96.2% of papers), while robustness optimization remains significantly underfocused (27.3% of papers), representing a critical research gap. This study identifies and prioritizes five research gaps, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices. This mapping study provides structured guidance for future research in LoRaWAN optimization and supports evidence-based decision-making in the field.