LoRaWAN for Smart Medical Emergency Service (EMS) Integrating with AI

DOI

10.2991/978-94-6239-616-6_81How to use a DOI?

Keywords

LoRaWAN; Artificial Intelligence; Emergency Medical Services; Wearable Sensors; Telemedicine; Edge AI; Smart Healthcare; Patient Triage

Abstract

Medical emergencies require quick action during the critical “golden hour” to avoid life-threatening consequences. However, traditional healthcare systems often face problems like delayed identification of health issues, a lack of real-time first-aid guidance, and late notifications to hospitals. These challenges become even more serious in rural and low-connectivity areas. Recent advancements in LoRaWAN (Long Range Wide Area Network), wearable IoT devices, and Artificial Intelligence (AI) have opened up new possibilities for real-time health monitoring and automated emergency response, even in remote locations. This paper provides a detailed survey of AI-enabled LoRaWAN architectures that aim to improve emergency medical services (EMS). The study looks at key technologies, including wearable health sensors, LoRaWAN-based communication protocols, AI algorithms for detecting anomalies and triaging patients, and telemedicine integration for doctor telepresence during emergencies. We review existing research, compare different LoRaWAN implementations for healthcare, and point out current challenges related to bandwidth limits, energy efficiency, data security, and system scalability. Additionally, we suggest a conceptual framework called SmartMed-LoRaWAN. This framework combines continuous health monitoring, intelligent emergency detection, AI-driven first-aid guidance, and remote teleconsultation to significantly cut down response times and enhance survival rates. Finally, this paper examines future research opportunities, such as using edge AI for low-latency emergency detection, applying federated learning for privacy-focused healthcare analytics, utilizing blockchain for safe data sharing, and developing strategies for scaling up LoRaWAN-enabled EMS in both urban and rural environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, 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 changes were made.

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TY  - CONF
AU  - R. Vijaya Prabhu
AU  - S. Balamurugan
AU  - R. Pradheep
AU  - K. Karthikeyan
PY  - 2026
DA  - 2026/03/31
TI  - LoRaWAN for Smart Medical Emergency Service (EMS) Integrating with AI
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1116
EP  - 1127
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_81
DO  - 10.2991/978-94-6239-616-6_81
ID  - Prabhu2026
ER  -