Title: Mobile network-enabled visualisation framework for real-time disaster science communication and response coordination

Authors: Ting Gao

Addresses: Institute of Disaster Prevention, Lang fang, Hebei, 065201, China

Abstract: This study proposes a comprehensive framework that integrates mobile network coverage prediction, artificial intelligence (AI), the Internet of Things (IoT), augmented reality (AR), and social media analytics to enhance real-time disaster communication and emergency response. The system utilises signal strength data and deep learning models to predict mobile network coverage and enable accurate remote environment mapping. A standard operating procedure (SOP)-based platform automates disaster response for fires, floods, and earthquakes through monitoring, control, and support modules. Augmented reality and gesture recognition facilitate real-time visualisation and interactive control via mobile interfaces. Social media and IoT data are processed using machine learning algorithms to detect emergencies and support informed decision-making. Simulated scenarios, including missile strikes and earthquakes, demonstrate the system's adaptability in managing urban resilience. Performance evaluations under varying traffic loads confirm the system's reliability and low latency, offering a scalable and efficient solution for modern disaster management.

Keywords: real-time visualisation; disaster management; mobile network communication; emergency response coordination; situational awareness; GIS-based mapping.

DOI: 10.1504/IJMNDI.2026.152395

International Journal of Mobile Network Design and Innovation, 2026 Vol.12 No.1, pp.42 - 56

Received: 09 Jul 2025
Accepted: 06 Oct 2025
Published online: 18 Mar 2026 *

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