
Research Article
Analyzing LoRaWAN's Challenges and Future Integration for Disaster Management
@INPROCEEDINGS{10.4108/eai.24-4-2026.2364847, author={Wenbo Zhang}, title={Analyzing LoRaWAN's Challenges and Future Integration for Disaster Management}, proceedings={Proceedings of the 3rd International Conference on Mechanics, Electronics Engineering and Automation, ICMEEA 2026, April 24-26, 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICMEEA}, year={2026}, month={9}, keywords={LoRaWAN Emergency Communications Reliability Scalability 5G Integration}, doi={10.4108/eai.24-4-2026.2364847} }- Wenbo Zhang
Year: 2026
Analyzing LoRaWAN's Challenges and Future Integration for Disaster Management
ICMEEA
EAI
DOI: 10.4108/eai.24-4-2026.2364847
Abstract
Traditional communication infrastructures are highly susceptible to disruption during disasters, leading to severe communication breakdowns in emergency response operations. Low-Power Wide-Area Networks (LPWANs), particularly LoRaWAN, therefore emerged as a promising alternative due to their long-range connectivity, low-power consumption, and capability for rapid ad-hoc deployment. Nevertheless, LoRaWAN’s ALOHA-based medium access introduces a conflict between its best-effort design and the mission-critical reliability demanded in emergency scenarios. This review systematically examines five case studies in industrial monitoring, fire detection, flood surveillance, and search and rescue (SAR) applications to identify the core challenges in practical LoRa deployments. The research results indicate that the reliability of LoRa is highly dependent on non-standard network architecture, optimized physical layer configuration, and environmental conditions. Scalability remains the primary constraint; a mass-alert event can trigger severe packet collisions and network instability. Bandwidth limitations also restrict LoRa to low-rate data, making high-throughput media infeasible. Therefore, Lora can serve as a stable foundation for emergency communications only through integration with 5G core infrastructures to ensure system-level reliability and through AI/ML-driven optimization to enhance energy and network efficiency.

