[Paper Review] Emergency Management Systems and Algorithms: a Comprehensive Survey

Huibo Bi, Erol Gelenbe|arXiv (Cornell University)|Jun 21, 2019

Evacuation and Crowd Dynamics167 references4 citations

TL;DR

This paper presents a comprehensive survey of emergency management systems and algorithms, focusing on emergency navigation and search and rescue planning. It reviews system architectures from human-driven to cloud-based, and algorithms ranging from cellular automata to biologically inspired models, identifying key challenges in cyber security, energy efficiency, and big data integration for future smart city applications.

ABSTRACT

Owing to the increasing frequency and destruction of natural and manmade disasters to modern highly-populated societies, emergency management, which provides solutions to prevent or address disasters, have drawn considerable research over the last few decades and become a multidisciplinary area. Because of its open and inclusive nature, new technologies always tend to influence, change or even revolutionise this research area. Hence, it is imperative to consolidate the state-of-the-art studies and knowledge to meet the research needs and identify the future research directions. The paper presents a comprehensive and systemic review of the existing research in the field of emergency management from both the system design aspect and algorithm engineering aspect. We begin with the history and evolution of the emergency management research. Then the two main research topics of this area, "emergency navigation" and "emergency search and rescue planning", are introduced and discussed. Finally, we suggest the emerging challenges and opportunities from system optimisation, evacuee behaviour modelling and optimisation, computing patterns, data analysis, energy and cyber security aspects.

Motivation & Objective

• To systematically review the evolution and current state of emergency management research, particularly in emergency navigation and search and rescue planning.
• To analyze the development of emergency management systems from human experience-driven models to modern cloud and IoT-based architectures.
• To categorize and evaluate emergency navigation and search & rescue algorithms across different computational paradigms and application contexts.
• To identify emerging challenges and opportunities in system optimization, evacuee behavior modeling, energy efficiency, cyber security, and big data analytics.
• To guide future research by outlining key technological trends and integration needs in smart cities and next-generation emergency systems.

Proposed method

• Conduct a systematic literature review of emergency management research from the 1950s to present, focusing on two core domains: emergency navigation and emergency search and rescue planning.
• Classify emergency navigation systems into five categories: human experience driven, static WSN-based, mixed WSN-based, cloud-based with WSN, and cloud-based with mobile phones.
• Categorize emergency navigation algorithms into off-line (e.g., cellular automata, social force, fluid-dynamics, game theory, agent-based models) and on-line (e.g., network flow, geometric, queueing models, potential-maintenance, biologically inspired, routing protocols, prediction-based).
• Review search and rescue systems including wearable computing-assisted systems and robotic systems, and analyze associated algorithms such as task assignment, resource allocation, and pathfinding.
• Integrate insights from emerging technologies such as edge/fog computing, CPN (Controlled Path Network) for self-defense against denial-of-service attacks, and G-network models for energy efficiency.
• Use big data analytics to model human behavior and collective decision-making from routinely collected sensor and device data for improved system design.

Experimental results

Research questions

• RQ1 How have emergency management systems evolved from reactive, experience-based models to proactive, technology-driven systems?
• RQ2 What are the key algorithmic approaches used in emergency navigation, and how do they differ between off-line and on-line computation paradigms?
• RQ3 What are the most effective system architectures for large-scale emergency evacuation, and how do they leverage cloud, WSN, and mobile technologies?
• RQ4 How can emerging technologies such as edge/fog computing, CPN, and G-network models enhance system resilience, energy efficiency, and security?
• RQ5 What role can big data analytics and behavioral modeling play in improving the accuracy and adaptability of future emergency management systems?

Key findings

• Emergency navigation has evolved from purely mathematical models like time-expanded network flows to complex, multi-agent systems using cellular automata, social force, and biologically inspired algorithms.
• Cloud-based and mobile phone-assisted systems show promise in reducing communication costs and improving scalability, especially when combined with mobile agent technology.
• CPN (Controlled Path Network) enables self-defense against denial-of-service attacks by enabling upstream packet dropping through full path tracking, offering a robust alternative to traditional IP protocols.
• Energy-efficient algorithms based on G-network models and dynamic programming can significantly extend the operational lifetime of wireless sensor networks in emergency systems.
• The integration of big data from IoT and smart city infrastructures enables more accurate modeling of evacuee behavior and collective decision-making, improving system responsiveness and planning accuracy.
• Future systems must combine edge/fog computing, self-aware networking, and adaptive algorithms to address challenges in communication reliability, energy constraints, and cyber threats during large-scale emergencies.