Short Communication - (2025) Volume 14, Issue 5
Received: 01-Sep-2025, Manuscript No. sndc-26-179813;
Editor assigned: 03-Sep-2025, Pre QC No. P-179813;
Reviewed: 17-Sep-2025, QC No. Q-179813;
Revised: 22-Sep-2025, Manuscript No. R-179813;
Published:
29-Sep-2025
, DOI: 10.37421/2090-4886.2025.14.356
Citation: Grant, Oliver. ”Sensor Networks: Disaster Monitoring, Early Warning, And Response.” Int J Sens Netw Data Commun 14 (2025):356.
Copyright: © 2025 Grant O. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
The critical role of sensor networks in enhancing disaster monitoring and management is extensively explored, highlighting how real-time data from distributed sensors can significantly improve early warning systems, situational awareness, and response coordination during emergencies like floods, earthquakes, and wildfires [1].
The deployment of diverse sensor types, including environmental, structural, and communication sensors, is discussed in detail, alongside the challenges and opportunities in data processing and analysis for effective decision-making. A comprehensive review of the application of IoT-based sensor networks for natural disaster prediction and mitigation details various sensor technologies and their integration for monitoring seismic activity, atmospheric conditions, and water levels [2].
This research emphasizes the importance of intelligent data fusion and machine learning algorithms to derive actionable insights from sensor data, thereby enabling proactive disaster management strategies and reducing societal impact. An intelligent sensor network for real-time flood monitoring and early warning systems is presented, integrating various sensors for water level, rainfall, and flow rate, transmitting data wirelessly to a central processing unit [3].
Advanced algorithms are employed to predict flood progression and issue timely alerts to vulnerable communities, significantly enhancing disaster preparedness and response efficiency, while also discussing the network's reliability and scalability in challenging environmental conditions. The use of heterogeneous sensor networks for wildfire detection and management is investigated, combining data from ground-based sensors (temperature, humidity, smoke) with aerial platforms (drones equipped with thermal cameras) to achieve early and accurate detection [4].
The work elaborates on data fusion techniques to minimize false alarms and improve the localization of fire outbreaks, emphasizing the potential of such integrated systems for improving firefighting strategies and resource allocation. A novel approach to seismic event monitoring using distributed sensor networks is introduced, focusing on enhancing the sensitivity and reliability of earthquake detection by deploying a large number of low-cost sensors [5].
The paper discusses the challenges of handling massive amounts of seismic data and proposes efficient processing techniques for real-time analysis and rapid alert generation, with the potential to improve seismic hazard assessment and preparedness being a key takeaway. The integration of wearable sensors into disaster response teams for improved situational awareness and personnel safety is examined, detailing how physiological data (heart rate, body temperature) and location tracking from wearable devices can provide commanders with real-time insights into the well-being and deployment status of responders [6].
The paper also addresses data security and privacy concerns associated with transmitting sensitive information from these sensors. A framework for utilizing drone-based sensor networks for rapid damage assessment after earthquakes is proposed, leveraging high-resolution imagery and LiDAR data collected by drones to identify structural damage and map affected areas [7].
The paper discusses the automated processing of this data using machine learning to provide quick and accurate damage reports, which are crucial for directing rescue efforts and allocating aid effectively in the immediate aftermath of a disaster. The communication infrastructure required for sensor networks in disaster management is focused on, exploring the challenges posed by damaged communication networks during emergencies and presenting solutions using resilient and ad-hoc communication protocols [8].
The article discusses the use of multiple communication technologies, including LoRaWAN, 5G, and satellite communication, to ensure reliable data transmission from sensors to command centers even in the most adverse conditions. Security and privacy challenges in sensor networks used for disaster monitoring are addressed, examining potential vulnerabilities and proposing cryptographic methods and access control mechanisms to protect sensitive data collected by sensors [9].
The paper emphasizes the need for secure data transmission and storage to maintain public trust and ensure the integrity of disaster management operations, especially when dealing with critical infrastructure information. Finally, the energy efficiency of sensor nodes in long-term disaster monitoring applications is evaluated, exploring various low-power sensing technologies and energy harvesting techniques to extend the operational lifetime of sensor networks deployed in remote or inaccessible areas [10].
The paper presents a comparative analysis of different energy management strategies and their impact on network performance, aiming to reduce maintenance costs and ensure continuous data availability during prolonged disaster events.
Sensor networks play a pivotal role in modern disaster management, offering unprecedented capabilities for real-time monitoring and rapid response. The integration of diverse sensor types allows for a comprehensive understanding of environmental conditions, structural integrity, and communication status during emergencies, thereby enhancing early warning systems and improving situational awareness. Research highlights how distributed sensor networks can significantly improve the coordination of relief efforts, enabling more effective decision-making in critical situations [1].
The application of IoT-based sensor networks for natural disaster prediction and mitigation is a growing area of interest, with studies detailing the use of various sensor technologies to monitor seismic activity, atmospheric changes, and water levels. The importance of intelligent data fusion and machine learning algorithms is underscored in extracting actionable insights from sensor data, facilitating proactive management strategies and reducing the overall impact of disasters on society [2].
In the context of flood management, intelligent sensor networks are being developed for real-time monitoring and early warning. These systems integrate sensors for water level, rainfall, and flow rate, transmitting data wirelessly to a central unit for advanced prediction and timely alerts. The reliability and scalability of such networks in challenging environments are crucial for effective disaster preparedness and response efficiency [3].
Wildfire detection and management are being transformed by heterogeneous sensor networks that combine ground-based sensors with aerial platforms. This integrated approach enables early and accurate detection, with data fusion techniques minimizing false alarms and improving fire localization. Such systems have the potential to revolutionize firefighting strategies and resource allocation [4].
Seismic event monitoring is being enhanced through distributed sensor networks that leverage a large number of low-cost sensors to improve detection sensitivity and reliability. The challenges associated with processing massive amounts of seismic data are being addressed with efficient techniques for real-time analysis and rapid alert generation, contributing to better seismic hazard assessment and preparedness [5].
The safety and situational awareness of disaster response teams are being improved through the integration of wearable sensors. These devices provide real-time physiological data and location tracking, offering commanders critical insights into the well-being and deployment status of responders. Addressing data security and privacy is paramount in this domain [6].
Rapid damage assessment after earthquakes is becoming more efficient with drone-based sensor networks. These networks utilize high-resolution imagery and LiDAR data to identify structural damage and map affected areas, with automated processing via machine learning providing quick and accurate reports crucial for guiding rescue efforts and aid allocation [7].
The resilience of communication infrastructure for sensor networks in disaster management is a significant concern, particularly when existing networks are damaged. Solutions involve resilient and ad-hoc communication protocols, utilizing technologies like LoRaWAN, 5G, and satellite communication to ensure reliable data transmission even under adverse conditions [8].
Security and privacy are critical considerations for sensor networks in disaster monitoring. Vulnerabilities are being addressed through cryptographic methods and access control mechanisms to protect sensitive data, ensuring the integrity of disaster management operations and maintaining public trust, especially concerning critical infrastructure information [9].
Finally, the energy efficiency of sensor nodes is vital for long-term disaster monitoring. Research focuses on low-power sensing technologies and energy harvesting techniques to extend operational lifetimes, particularly in remote areas. Comparative analyses of energy management strategies are crucial for ensuring continuous data availability during prolonged events and reducing maintenance costs [10].
Sensor networks are crucial for disaster monitoring and management, enabling real-time data collection for early warning, situational awareness, and response coordination. Diverse sensor types are utilized for floods, earthquakes, and wildfires, with advancements in data processing and machine learning aiding prediction and mitigation. IoT-based systems offer comprehensive monitoring capabilities, while intelligent networks for floods provide timely alerts. Heterogeneous networks, combining ground and aerial sensors, improve wildfire detection, and distributed networks enhance seismic event monitoring. Wearable sensors increase responder safety and awareness, and drone-based systems facilitate rapid post-earthquake damage assessment. Resilient communication infrastructure is vital, and robust security and privacy measures are essential for data integrity and public trust. Energy efficiency is a key consideration for long-term deployment, ensuring continuous data availability and reducing operational costs.
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