Jul 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
An IoT-enabled disaster management system that integrates distributed sensors, wireless communication networks, cloud computing platforms, and machine learning techniques to improve disaster preparedness, detection, response, and recovery is presented.
Abstract
The increasing frequency and intensity of natural and man-made disasters have highlighted the necessity for intelligent disaster management systems capable of providing rapid response and accurate situational awareness. Conventional disaster management approaches often rely on manual observations, fragmented communication infrastructures, and delayed reporting mechanisms, which can significantly reduce the effectiveness of emergency response operations. The emergence of the Internet of Things (IoT) has introduced new opportunities for real-time monitoring, data acquisition, predictive analytics, and automated decision-making. This paper presents an IoT-enabled disaster management system that integrates distributed sensors, wireless communication networks, cloud computing platforms, and machine learning techniques to improve disaster preparedness, detection, response, and recovery. The proposed framework continuously monitors environmental and structural parameters, analyzes collected information through intelligent algorithms, and generates early warnings for emergency authorities and affected communities. The system aims to minimize casualties, reduce property damage, and enhance coordination among disaster response agencies. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced operational efficiency when compared with conventional disaster management systems. The proposed solution offers a scalable, reliable, and cost-effective approach for building resilient smart cities and disaster-resistant communities.
Keywords— Internet of Things, Disaster Management, Smart Cities, Early Warning Systems, Machine Learning, Cloud Computing, Emergency Response.
Disasters triggered by hydro-meteorological, geophysical, and climatic hazards are increasing in frequency and severity, straining the capacity of conventional disaster risk management (DRM) systems that rely on manual data collection and delayed decision-making [1], [5]. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), and Geographic Information Systems (GIS) have opened new possibilities for building smart, data-driven, and anticipatory disaster management systems [3], [14]. This paper presents a systematic review of the literature published mainly between 2020 and 2026 that examines how AI, IoT, and GIS are being integrated to support hazard prediction, real-time monitoring, spatial risk assessment, early warning, and post-disaster response. Fifty-seven peer-reviewed and indexed sources were synthesised following a structured review protocol. The review identifies five major integration themes: (i) sensor-driven early warning networks, (ii) geospatial machine-learning hazard susceptibility mapping, (iii) UAV and remote-sensing based damage assessment, (iv) social-media and big-data situational awareness, and (v) digital-twin-enabled smart-city resilience platforms. The paper further develops a conceptual AI-IoT-GIS integration architecture, presents mathematical formulations commonly used for spatial risk indexing, sensor network reliability, and machine-learning performance evaluation, and illustrates these formulations with worked numerical examples, tables, and charts. The review concludes that while integration of these three technologies significantly improves prediction accuracy and response times, challenges remain around data interoperability, energy-constrained sensor networks, algorithmic bias, and the digital divide affecting adoption in low-resource regions [2], [15].
Chirag Patel· Journal of Commerce, Economi...· 0 citations
The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
Alan Bundy, Karen Spärck Jones· International Journal of Mod...· 0 citations
Natural disasters are happening more often and with more force, which puts a lot of stress on disaster management systems. They require fast, reliable and sustainable technologies. In conventional centralized systems, there are problems with latency, network congestion and a requirement for continuous connectivity. These can slow down the early detection and rapid response. In this study, the Smart Disaster Alert Response System (SDARS), a new approach that integrates cloud computing, edge computing, and green computing to enhance resilience, cut down latency and ensure sustainability. SDARS relies on IoT sensors that constantly monitor the environment, and edge nodes that do initial data processing to provide real-time alerts with low latency. The cloud allows for big data analysis to be conducted to gain predictive knowledge and optimise resource allocation. Energy-efficient (green computing) techniques are also used to conserve energy and extend the lifespan of devices in low-energy environments. The experimental evaluation of SDARS reveals that it can raise alarms in less than a second, consumes 35% less energy compared to conventional systems and continues to operate despite network partition. In conclusion, the proposed model demonstrates how distributed computing techniques with energy-aware techniques can result in scalable and sustainable disaster response systems.
P. R, R. V., R. M et al.· International Journal of Ele...· 0 citations
A dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data based on a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling.
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
The rapid expansion of the Internet of Things (IoT) has enabled the development of intelligent systems capable of addressing complex challenges across public safety, agriculture, and digital security. However, most existing solutions are designed for individual applications and lack a unified framework that integrates multiple real-world domains with mathematical decision-making. This study proposes a unified mathematical and IoT-based predictive framework that combines smart fire detection, cybersecurity, privacy protection, precision irrigation, and cybercrime pattern analysis within a single architecture. The proposed framework employs interconnected IoT devices to collect and transmit real-time data from distributed environments, while mathematical modeling and predictive analysis support timely decision-making and efficient resource utilization. In the fire detection module, environmental sensors monitor parameters such as temperature, smoke, and gas concentration to enable early hazard identification. The precision irrigation module utilizes soil and weather information to optimize water distribution and improve agricultural productivity. To strengthen cyberspace security, the framework incorporates data protection mechanisms and analyzes cybercrime patterns to identify potential threats and support preventive actions. The integration of these components demonstrates how mathematical analysis and IoT technologies can enhance system reliability, operational efficiency, and decision accuracy across diverse application domains. The proposed framework provides a scalable and adaptable foundation for the development of secure and intelligent smart systems suitable for future smart cities, agriculture, and critical infrastructure.
Manish Kumar Singh, Pankaj Kumar Gupta, Rohit Kumar et al.· International journal of com...· 0 citations
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