DJCCMT

Delta Journal of Computing, Communications and Media Technologies

ISSN:3092-8478

Advancing research and innovation at the intersection of computing technology and media. A publication of Southern Delta University, Ozoro.

Delta Journal of Computing, Communications and Media Technologies(DJCCMT) is an open access double-blind peer reviewed and refereed Journal that brings together reasoned thoughts, research, and industry practice in areas of Computing, Artificial Intelligence, Robotics, System Engineering, Data Science, Analytics, Embedded Systems, Information and System Security, Media Studies, Communication Technologies, Information Science, Library Science, Educational Technologies, Applied Computing, and related disciplines in a reader-friendly format. The Journal is published online monthly with print version issue in February, May, August and November.

Delta Journal of Computing, Communications and Media Technologies

Volume 1 · Issue 1 · December 2024
Title of Paper

BUILDING EMERGENCY RESPONSE SYSTEMS: AI-DRIVEN COMMUNICATION AND COORDINATION

Abstract

Effective emergency response hinges on rapid communication, coordinated resource management, and data-driven decision-making—areas where traditional systems often fall short due to siloed agencies, delayed alerts, and limited situational awareness. This paper presents AI-ERS, an AI-implemented Emergency Response System designed to enhance interoperability among first responders, optimize resource allocation, and accelerate decision cycles. AI-ERS integrates Internet of Things (IoT) sensors for real-time environmental monitoring, machine learning models for incident prediction, and natural language processing to triage incoming reports. A mixed-integer optimization module forecasts resource needs and dynamically reallocates assets across agencies. In simulation trials, AI-ERS reduced average response time by 23% and improved resource utilization by 17% compared to conventional dispatch systems. We also examine ethical considerations—data privacy, algorithmic fairness, and accountability—and infrastructure requirements for scalable deployment. Finally, we outline a roadmap for future research, including multi-agent reinforcement learning for autonomous coordination and blockchain-based audit trails for secure data sharing. Our findings demonstrate that AI-driven emergency management can substantially elevate operational efficiency, resilience, and community safety in diverse disaster scenarios.

Authors

Oyewale Mojeed Adebowale, Nwodi Ngozi Florence, Alao Taiwo Omoniyi, Wilson Nwankwo

Keywords

Emergency response systems, Disaster management, Communication, Coordination algorithms, Optimization IoT Integration.

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