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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Alksasbeh, Malek Z.
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Computer Science & IT Engineering

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Life balloon: a paradigm shift in earthquake safety-intelligent IoT detection and protection system for optimal resilience Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Z.; Alemerien, Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp987-997

Abstract

Internet of things (IoT) applications for environmental monitoring have greatly improved due to advances in hardware and software technologies. Given the significant economic and societal impacts of earthquakes, there is an increasing need to develop effective earthquake early warning systems (EEWS). However, designing such intelligent systems remains challenging because of inefficient classification methods and limitations in high-fidelity sensing capabilities. To reduce the devastating effects of earthquakes, this paper proposes an earthquake detection and protection system. The system’s primary function is to detect seismic signals and activate a specially designed airbag (life balloon) unit that protects occupants in apartment buildings. In addition, the unit helps maintain necessary oxygen levels, thereby improving occupant safety during seismic events. The proposed system also includes a communication method that transmits critical information about the affected area to relevant parties. Early data transmission enables rapid response and guides the efficient deployment of required resources, making aftershock management more effective. By combining advanced sensor technologies with efficient communication methods, the proposed system aims to enhance safety and emergency management while providing comprehensive protection and support during seismic events. Experimental results show that the proposed method achieves approximately 95% sensitivity and 94.2% accuracy.
Co-Authors Abusaleh, Sumaya Alemerien, Khalid Alrawashdeh, Tawfiq