Role of IoT in Disaster Monitoring and Response: A Comprehensive Review
DOI:
https://doi.org/10.21015/vtse.v13i3.2193Abstract
In an age where natural and man-made disasters are increasing in frequency and intensity, the impact of technology is expanding in disaster management. One of these transformative technologies is the Internet of Things (IoT) in disaster management. IoT-based disaster management systems involve monitoring environmental conditions, such as temperature, humidity, seismic activity, rainfall, water levels, or gas concentrations, by utilizing sensor nodes deployed in the field to autonomously record relevant data. This recorded data is then transferred to a central sink node or base station, performing as a gateway node for further communication, data collection, and smart decision-making. This review article comprehensively explores the critical aspects of the Internet of Things (IoT) in off-site disaster monitoring and management. It offers a broad overview of IoT-enabled disaster management applicable across various disaster types, including earthquakes, floods, forest fires, and nuclear hazards, consisting of sensor networks, supported by wireless communication, cloud computing, and advanced data analytics, that provide real-time understanding, foresight, and automated responses across all four phases of disaster management, including mitigation, preparedness, response, and recovery. It also highlights the importance of integrating IoT with emergent technologies such as artificial intelligence (AI), edge computing, big data, and 5G, which can significantly increase the responsiveness, efficiency, and resilience of disaster management systems.
References
V. D. Gowda et al., “Dynamic Disaster Management with Real-Time IoT Data Analysis and Response,” in Proc. 2024 Int. Conf. Automation and Computation (AUTOCOM), 2024.
K. Sharma et al., “A disaster management framework using Internet of Things-based interconnected devices,” Mathematical Problems in Engineering, vol. 2021, no. 1, p. 9916440, 2021.
M. Perry, “Natural disaster management planning: A study of logistics managers responding to the tsunami,” Int. J. Phys. Distrib. Logist. Manag., vol. 37, no. 5, pp. 409–433, 2007. DOI: https://doi.org/10.1108/09600030710758455
M. Onagh, V. Kumra, and P. K. Rai, “Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method,” Int. J. Geol., Earth Environ. Sci., vol. 2, no. 2, pp. 102–120, 2012.
R. Margesson and M. Taft-Morales, Haiti Earthquake: Crisis and Response. Washington, DC: Congressional Research Service, 2010.
M. Cubrinovski et al., “Geotechnical aspects of the 22 February 2011 Christchurch earthquake,” Bull. N. Z. Soc. Earthq. Eng., vol. 44, no. 4, pp. 205–226, 2011.
A. Kaiser et al., “The Mw 6.2 Christchurch earthquake of February 2011: preliminary report,” N. Z. J. Geol. Geophys., vol. 55, no. 1, pp. 67–90, 2012. DOI: https://doi.org/10.1080/00288306.2011.641182
F. Tajima, J. Mori, and B. L. Kennett, “A review of the 2011 Tohoku-Oki earthquake (Mw 9.0): Large-scale rupture across heterogeneous plate coupling,” Tectonophysics, vol. 586, pp. 15–34, 2013. DOI: https://doi.org/10.1016/j.tecto.2012.09.014
M. L. Hall et al., “The 2015 Nepal earthquake disaster: lessons learned one year on,” Public Health, vol. 145, pp. 39–44, 2017. DOI: https://doi.org/10.1016/j.puhe.2016.12.031
K. Goda et al., “The 2015 Gorkha Nepal earthquake: insights from earthquake damage survey,” Front. Built Environ., vol. 1, p. 8, 2015. DOI: https://doi.org/10.3389/fbuil.2015.00008
M. Mavrouli, S. Mavroulis, E. Lekkas, and A. Tsakris, “An emerging health crisis in Turkey and Syria after the earthquake disaster on 6 February 2023: Risk factors, prevention and management of infectious diseases,” in Healthcare, 2023.
M. Atanasova, P. Raykova, and H. Nikolov, “Determining the deformations of the Earth's surface after the earthquakes in Turkey-Syria of 06 February 2023—Initial results,” in Proc. Bulgarian Acad. Sci., 2023.
E. N. Cinar, A. Abbara, and E. Yilmaz, “Earthquakes in Turkey and Syria—collaboration is needed to mitigate longer term risks to health,” BMJ, 2023.
N. Kishore et al., “Mortality in Puerto Rico after Hurricane Maria,” N. Engl. J. Med., vol. 379, no. 2, pp. 162–170, 2018. DOI: https://doi.org/10.1056/NEJMsa1803972
E. Meléndez and J. Hinojosa, Estimates of Post-Hurricane Maria Exodus from Puerto Rico, 2017.
E. Mongo, E. Cambaza, and R. Nhambire, “Cyclone Idai in Central Mozambique (2019): Evaluation of health services,” 2020, p. 67.
Federal Communications Commission, Emergency Communications, 2023.
World Meteorological Organization, Climate Change and Extreme Weather Impacts Hit Asia Hard, Apr. 23, 2024.
V. M. Cvetković, R. Renner, B. Aleksova, and T. Lukić, “Geospatial and temporal patterns of natural and man-made (technological) disasters (1900–2024): Insights from different socio-economic and demographic perspectives,” Appl. Sci., vol. 14, no. 18, p. 8129, 2024.
M. Kamruzzaman, N. I. Sarkar, J. Gutierrez, and S. K. Ray, “A study of IoT-based post-disaster management,” in Proc. 2017 Int. Conf. Inf. Netw. (ICOIN), 2017, pp. 406–411. DOI: https://doi.org/10.1109/ICOIN.2017.7899468
M. Krichen, M. S. Abdalzaher, M. Elwekeil, and M. M. Fouda, “Managing natural disasters: An analysis of technological advancements, opportunities, and challenges,” Internet Things Cyber-Phys. Syst., vol. 4, pp. 99–109, 2024.
United Nations Office for Disaster Risk Reduction (UNDRR), Global Assessment Report on Disaster Risk Reduction (GAR), 2025.
P. P. Jayaraman et al., “Internet of things platform for smart farming: Experiences and lessons learnt,” Sensors, vol. 16, no. 11, p. 1884, 2016. DOI: https://doi.org/10.3390/s16111884
A. Kaloxylos et al., “Farm management systems and the Future Internet era,” Comput. Electron. Agric., vol. 89, pp. 130–144, 2012. DOI: https://doi.org/10.1016/j.compag.2012.09.002
P. Suresh, J. V. Daniel, V. Parthasarathy, and R. Aswathy, “A state of the art review on the Internet of Things (IoT) history, technology and fields of deployment,” in Proc. 2014 Int. Conf. Sci. Eng. Manag. Res. (ICSEMR), 2014, pp. 1–8. DOI: https://doi.org/10.1109/ICSEMR.2014.7043637
N. Sharma, M. Shamkuwar, and I. Singh, “The history, present and future with IoT,” in Internet of Things and Big Data Analytics for Smart Generation, Springer, 2018, pp. 27–51.
L. DeNardis, The Internet in Everything. New Haven, CT: Yale Univ. Press, 2020.
O. Mphale, K. N. Gorejena, and O. Nojila, “The future of things: A comprehensive overview of Internet of Things history, definitions, technologies, architectures, communication and beyond,” J. Inf. Syst. Informatics, vol. 6, no. 2, pp. 1263–1286, 2024.
A. H. Sodhro, S. Pirbhulal, and V. H. C. De Albuquerque, “Artificial intelligence-driven mechanism for edge computing-based industrial applications,” IEEE Trans. Ind. Informatics, vol. 15, no. 7, pp. 4235–4243, 2019.
A. Singh, S. C. Satapathy, A. Roy, and A. Gutub, “AI-based mobile edge computing for IoT: Applications, challenges, and future scope,” Arab. J. Sci. Eng., vol. 47, no. 8, pp. 9801–9831, 2022.
S. Misra, A. K. Tyagi, V. Piuri, and L. Garg, Artificial Intelligence for Cloud and Edge Computing. Cham, Switzerland: Springer, 2022.
J. Zeng, C. Li, and L.-J. Zhang, “A face recognition system based on cloud computing and AI edge for IoT,” in Edge Computing – EDGE 2018: 2nd Int. Conf., Held as Part of the Services Conf. Federation, SCF 2018, Seattle, WA, USA, Jun. 25–30, 2018, Proc. 2. Cham, Switzerland: Springer, 2018, pp. 90–103.
J. Saqlain, IoT and 5G: History, Evolution and Its Architecture, Their Compatibility and Future, 2018.
S. Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A literature review,” J. Comput. Commun., vol. 3, no. 5, pp. 164–173, 2015. DOI: https://doi.org/10.4236/jcc.2015.35021
I. Analytics, Global IoT Market Forecast (in Billions of Connected IoT Devices), 2024.
B. L. Risteska Stojkoska and K. V. Trivodaliev, “A review of Internet of Things for smart home: Challenges and solutions,” J. Cleaner Prod., vol. 140, pp. 1454–1464, 2017. DOI: https://doi.org/10.1016/j.jclepro.2016.10.006
H. Ghayvat, S. Mukhopadhyay, X. Gui, and N. Suryadevara, “WSN- and IoT-based smart homes and their extension to smart buildings,” Sensors, vol. 15, no. 5, pp. 10350–10379, 2015. DOI: https://doi.org/10.3390/s150510350
A. Goudarzi et al., “A survey on IoT-enabled smart grids: Emerging, applications, challenges, and outlook,” Energies, vol. 15, no. 19, p. 6984, 2022.
F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “A review of machine learning and IoT in smart transportation,” Future Internet, vol. 11, no. 4, p. 94, 2019.
A. Tzounis, N. Katsoulas, T. Bartzanas, and C. Kittas, “Internet of Things in agriculture, recent advances and future challenges,” Biosyst. Eng., vol. 164, pp. 31–48, 2017. DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007
D. C. Nguyen et al., “Federated learning meets blockchain in edge computing: Opportunities and challenges,” IEEE Internet Things J., vol. 8, no. 16, pp. 12806–12825, 2021.
X. Wang et al., “In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Netw., vol. 33, no. 5, pp. 156–165, 2019.
M. A. Khan et al., “Smart buildings: Federated learning-driven secure, transparent and smart energy management system using XAI,” Energy Rep., vol. 13, pp. 2066–2081, 2025.
S. H. Shah and I. Yaqoob, “A survey: Internet of Things (IoT) technologies, applications and challenges,” in Proc. 2016 IEEE Smart Energy Grid Eng. (SEGE), 2016, pp. 381–385. DOI: https://doi.org/10.1109/SEGE.2016.7589556
M. A. Khan et al., “Optimizing smart home energy management for sustainability using machine learning techniques,” Discover Sustainability, vol. 5, no. 1, p. 430, 2024.
S. K. Routray and H. M. Hussein, “Satellite based IoT networks for emerging applications,” arXiv preprint arXiv:1904.00520, 2019.
S. K. Routray et al., “Satellite based IoT for mission critical applications,” in Proc. 2019 Int. Conf. Data Sci. Commun. (IconDSC), 2019, pp. 1–6.
V. S. Chippalkatti and R. C. Biradar, “Review of satellite based Internet of Things and applications,” Turk. J. Comput. Math. Educ., vol. 12, no. 12, pp. 758–766, 2021.
P. Kang and J. Jo, “Benchmarking modern edge devices for AI applications,” IEICE Trans. Inf. Syst., vol. 104, no. 3, pp. 394–403, 2021.
A. Garcia-Perez, R. Miñón, A. I. Torre-Bastida, and E. Zulueta-Guerrero, “Analysing edge computing devices for the deployment of embedded AI,” Sensors, vol. 23, no. 23, p. 9495, 2023.
S. Balaji, K. Nathani, and R. Santhakumar, “IoT technology, applications and challenges: a contemporary survey,” Wireless Personal Communications, vol. 108, pp. 363–388, 2019.
N. F. Syed et al., “Zero trust architecture (ZTA): A comprehensive survey,” IEEE Access, vol. 10, pp. 57143–57179, 2022.
A. Orsino et al., “Exploiting D2D communications at the network edge for mission-critical IoT applications,” in Proc. Eur. Wireless 2017; 23th Eur. Wireless Conf., 2017.
C. Zhang et al., “A survey on federated learning,” Knowledge-Based Systems, vol. 216, p. 106775, 2021.
J. Wen et al., “A survey on federated learning: Challenges and applications,” International Journal of Machine Learning and Cybernetics, vol. 14, no. 2, pp. 513–535, 2023.
IEEE Standards Association, P2413.2 – Standard for a Reference Architecture for Power Distribution IoT (PDIoT), 2019.
C. Coetzee and D. Van Niekerk, “Tracking the evolution of the disaster management cycle: A general system theory approach: Original research,” Jàmbá: Journal of Disaster Risk Studies, vol. 4, no. 1, pp. 1–9, 2012. DOI: https://doi.org/10.4102/jamba.v4i1.54
H. L. Tay, R. Banomyong, P. Varadejsatitwong, and P. Julagasigorn, “Mitigating risks in the disaster management cycle,” Advances in Civil Engineering, vol. 2022, no. 1, p. 7454760, 2022.
D. Ben Arbia et al., “Enhanced IoT-based end-to-end emergency and disaster relief system,” Journal of Sensor and Actuator Networks, vol. 6, no. 3, p. 19, 2017. DOI: https://doi.org/10.3390/jsan6030019
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The rising role of big data analytics and IoT in disaster management: Recent advances, taxonomy and prospects,” IEEE Access, vol. 7, pp. 54595–54614, 2019.
R. de F. Bail et al., “Internet of things in disaster management: Technologies and uses,” Environmental Hazards, vol. 20, no. 5, pp. 493–513, 2021.
S. M. S. Mohd Daud et al., “Applications of drone in disaster management: A scoping review,” Science & Justice, vol. 62, no. 1, pp. 30–42, 2022.
A. Sinha et al., “Impact of internet of things (IoT) in disaster management: A task-technology fit perspective,” Annals of Operations Research, vol. 283, no. 1, pp. 759–794, 2019. DOI: https://doi.org/10.1007/s10479-017-2658-1
F. Zeng, C. Pang, and H. Tang, “Sensors on the internet of things systems for urban disaster management: A systematic literature review,” Sensors, vol. 23, no. 17, p. 7475, 2023.
World Meteorological Organization, “Economic costs of weather-related disasters soars but early warnings save lives,” May 23, 2023.
A. Adeel et al., “A survey on the role of wireless sensor networks and IoT in disaster management,” in Geological Disaster Monitoring Based on Sensor Networks, T. S. Durrani, W. Wang, and S. M. Forbes, Eds. Singapore: Springer, 2019, pp. 57–66.
A. Alphonsa and G. Ravi, “Earthquake early warning system by IoT using wireless sensor networks,” in Proc. 2016 Int. Conf. Wireless Communications, Signal Processing and Networking (WiSPNET), 2016. DOI: https://doi.org/10.1109/WiSPNET.2016.7566327
M. Esposito et al., “Recent advances in internet of things solutions for early warning systems: A review,” Sensors, vol. 22, no. 6, p. 2124, 2022.
P. P. Ray, M. Mukherjee, and L. Shu, “Internet of Things for disaster management: State-of-the-art and prospects,” IEEE Access, vol. 5, pp. 18818–18835, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2752174
S. Sood, R. Sandhu, K. Singla, and V. Chang, “IoT, big data and HPC based smart flood management framework,” Sustainable Computing: Informatics and Systems, vol. 20, 2017. DOI: https://doi.org/10.1016/j.suscom.2017.12.001
A. Haque and H. Soliman, “Smart wireless sensor networks with virtual sensors for forest fire evolution prediction using machine learning,” Electronics, vol. 14, no. 2, p. 223, 2025.
S. Poslad et al., “A semantic IoT early warning system for natural environment crisis management,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 2, pp. 246–257, 2015. DOI: https://doi.org/10.1109/TETC.2015.2432742
R. Gaire et al., “Internet of Things (IoT) and cloud computing enabled disaster management,” in Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things, R. Ranjan et al., Eds. Cham, Switzerland: Springer International Publishing, 2020, pp. 273–298.
M. S. Abdalzaher, H. A. Elsayed, M. M. Fouda, and M. M. Salim, “Employing machine learning and IoT for earthquake early warning system in smart cities,” Energies, vol. 16, no. 1, p. 495, 2023.
R. M. Allen, Q. Kong, and R. Martin-Short, “The MyShake platform: A global vision for earthquake early warning,” Pure and Applied Geophysics, vol. 177, no. 4, pp. 1699–1712, 2020.
Q. Kong, S. Patel, A. Inbal, and R. M. Allen, “MyShake: Detecting and characterizing earthquakes with a global smartphone seismic network,” arXiv preprint arXiv:1904.09755, 2019.
A. Koubâa et al., “Deepbrain: Experimental evaluation of cloud-based computation offloading and edge computing in the internet-of-drones for deep learning applications,” Sensors, vol. 20, no. 18, p. 5240, 2020.
R. Tehseen, M. S. Farooq, and A. Abid, “A framework for the prediction of earthquake using federated learning,” PeerJ Computer Science, vol. 7, p. e540, 2021.
G. Pughazhendhi, A. Raja, P. Ramalingam, and D. K. Elumalai, “Earthosys—Tsunami prediction and warning system using machine learning and IoT,” in Proc. Int. Conf. Computational Intelligence and Data Engineering (ICCIDE 2018), 2019, Springer.
I. Khan, M. Pandey, and Y.-W. Kwon, “An earthquake alert system based on a collaborative approach using smart devices,” in Proc. 2021 IEEE/ACM 8th Int. Conf. Mobile Software Engineering and Systems (MobileSoft), 2021, IEEE.
H. Zhai and Y. Wang, “Design and implementation of earthquake information publishing system based on mobile computing and machine learning technology in GIS,” Journal of Interconnection Networks, vol. 22, no. 03, p. 2145018, 2022.
M. S. Abdalzaher et al., “A deep learning model for earthquake parameters observation in IoT system-based earthquake early warning,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8412–8424, 2021.
S. Sarkar, A. Roy, S. Kumar, and B. Das, “Seismic intensity estimation using multilayer perceptron for onsite earthquake early warning,” IEEE Sensors Journal, vol. 22, no. 3, pp. 2553–2563, 2021.
I. Khan and Y.-W. Kwon, “P-detector: Real-time P-wave detection in a seismic waveform recorded on a low-cost MEMS accelerometer using deep learning,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
E. Bassetti and E. Panizzi, “Earthquake detection at the edge: IoT crowdsensing network,” Information, vol. 13, no. 4, p. 195, 2022.
J. Lee, I. Khan, S. Choi, and Y.-W. Kwon, “A smart IoT device for detecting and responding to earthquakes,” Electronics, vol. 8, no. 12, p. 1546, 2019.
P. Sreevidya, C. Abhilash, J. Paul, and G. Rejithkumar, “A machine learning-based early landslide warning system using IoT,” in Proc. 2021 4th Biennial Int. Conf. Nascent Technologies in Engineering (ICNTE), 2021, IEEE.
A. Wu, J. Lee, I. Khan, and Y.-W. Kwon, “CrowdQuake+: Data-driven earthquake early warning via IoT and deep learning,” in Proc. 2021 IEEE Int. Conf. Big Data (Big Data), 2021.
M. Falanga et al., “Semantically enhanced IoT-oriented seismic event detection: An application to Colima and Vesuvius volcanoes,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9789–9803, 2022.
T. Clements, “Earthquake detection with tinyML,” Seismological Society of America, vol. 94, no. 4, pp. 2030–2039, 2023.
K. Fauvel et al., “A distributed multi-sensor machine learning approach to earthquake early warning,” in Proc. AAAI Conf. Artif. Intell., 2020.
I. Khan, S. Choi, and Y.-W. Kwon, “Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method,” Sensors, vol. 20, no. 3, p. 800, 2020.
C. A. Hernández-Gutiérrez et al., “IoT-enabled system for detection, monitoring, and tracking of nuclear materials,” Electronics, vol. 12, no. 14, p. 3042, 2023.
M. I. Ahmad et al., “Ionizing radiation monitoring technology at the verge of internet of things,” Sensors, vol. 21, no. 22, p. 7629, 2021.
A. Abimanyu, R. Akmalia, and M. Salam, “Design of IoT-based radiation monitor area for nuclear and radiological emergency preparedness system in Yogyakarta nuclear area,” in J. Phys.: Conf. Ser., 2020, IOP Publishing.
M. Saifullah, I. S. Bajwa, M. Ibrahim, and M. Asghar, “IoT‐enabled intelligent system for the radiation monitoring and warning approach,” Mobile Inf. Syst., vol. 2022, no. 1, p. 2769958, 2022.
M. Siddique, T. Ahmed, and M. S. Husain, “Flood monitoring and early warning systems—An IoT based perspective,” EAI Endorsed Trans. Internet Things, vol. 9, no. 2, 2023.
W. M. Shah, F. Arif, A. Shahrin, and A. Hassan, “The implementation of an IoT-based flood alert system,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, 2018.
D. S. Rani, G. Jayalakshmi, and V. P. Baligar, “Low cost IoT based flood monitoring system using machine learning and neural networks: flood alerting and rainfall prediction,” in Proc. 2nd Int. Conf. Innov. Mech. Ind. Appl. (ICIMIA), 2020, IEEE.
M. Wajid et al., “Flood prediction system using IoT & artificial neural network,” VFAST Trans. Softw. Eng., vol. 12, no. 1, pp. 210–224, 2024. DOI: https://doi.org/10.21015/vtse.v12i1.1603
S. Nezhadbasaidu et al., “A flood expert system using machine learning and IoT: warning, detection, and prediction,” Int. J. Syst. Assur. Eng. Manag., pp. 1–17, 2025.
M. Anbarasan et al., “Detection of flood disaster system based on IoT, big data and convolutional deep neural network,” Comput. Commun., vol. 150, pp. 150–157, 2020.
A. Sharma et al., “An IoT-based forest fire detection system: design and testing,” Multimed. Tools Appl., vol. 83, no. 13, pp. 38685–38710, 2024.
A. Tehseen et al., “Formal modeling of IoT and drone-based forest fire detection and counteraction system,” Electronics, vol. 11, no. 1, p. 128, 2021.
M. T. Basu, R. Karthik, J. Mahitha, and V. L. Reddy, “IoT based forest fire detection system,” Int. J. Eng. Technol., vol. 7, no. 2.7, pp. 124–126, 2018. DOI: https://doi.org/10.14419/ijet.v7i2.7.10277
J. Desikan et al., “Dempster Shafer-empowered machine learning-based scheme for reducing fire risks in IoT-enabled industrial environments,” IEEE Access, vol. 13, pp. 46546–46567, 2025.
A. Adeel et al., “A survey on the role of wireless sensor networks and IoT in disaster management,” Geol. Disaster Monit. Based Sens. Netw., pp. 57–66, 2019.
B. Meenakshi et al., “Wireless sensor networks for disaster management and emergency response using SVM classifier,” in Proc. 2nd Int. Conf. Smart Technol. Smart Nation (SmartTechCon), 2023, IEEE.
A. S. Nandan, S. Singh, A. Malik, and R. Kumar, “A green data collection & transmission method for IoT-based WSN in disaster management,” IEEE Sens. J., vol. 21, no. 22, pp. 25912–25921, 2021.
H. Kaur, R. S. Sawhney, and N. Komal, “Wireless sensor networks for disaster management,” Int. J. Adv. Res. Comput. Eng. Technol., vol. 1, no. 5, pp. 2278–1323, 2012.
M. Biabani, H. Fotouhi, and N. Yazdani, “An energy-efficient evolutionary clustering technique for disaster management in IoT networks,” Sensors, vol. 20, no. 9, p. 2647, 2020.
B. Mostefa and G. Abdelkader, “A survey of wireless sensor network security in the context of Internet of Things,” in Proc. 4th Int. Conf. Inf. Commun. Technol. Disaster Manag. (ICT-DM), 2017, IEEE. DOI: https://doi.org/10.1109/ICT-DM.2017.8275691
R. Damaševičius, N. Bacanin, and S. Misra, “From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management,” J. Sens. Actuator Netw., vol. 12, no. 3, p. 41, 2023.
H. N. Saha et al., “Disaster management using Internet of Things,” in Proc. 8th Annu. Ind. Autom. Electromech. Eng. Conf. (IEMECON), 2017, IEEE. DOI: https://doi.org/10.1109/IEMECON.2017.8079566
E. Migabo, K. Djouani, A. Kurien, and T. Olwal, “A comparative survey study on LPWA networks: LoRa and NB-IoT,” in Proc. Future Technol. Conf. (FTC), Vancouver, BC, Canada, 2017.
K. Ali et al., “Internet of Things (IoT) considerations, requirements, and architectures for disaster management system,” Performability Internet Things, pp. 111–125, 2019.
R. Gaire et al., “Internet of Things (IoT) and cloud computing enabled disaster management,” Handb. Integr. Cloud Comput., Cyber Phys. Syst. Internet Things, pp. 273–298, 2020.
S. Ugwuanyi, G. Paul, and J. Irvine, “Survey of IoT for developing countries: Performance analysis of LoRaWAN and cellular NB-IoT networks,” Electronics, vol. 10, no. 18, p. 2224, 2021.
K. A. Al-Sammak et al., “Optimizing IoT energy efficiency: Real-time adaptive algorithms for smart meters with LoRaWAN and NB-IoT,” Energies, vol. 18, no. 4, p. 987, 2025.
A. Girma et al., “IoT-enabled autonomous system collaboration for disaster-area management,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1249–1262, 2020.
P. Chinnasamy, et al., “Design and Implementation of an IoT-based Emergency Alert and GPS Tracking System using MQTT and GSM/GPS Module,” in Proc. 2025 5th Int. Conf. Trends Material Sci. Inventive Mater. (ICTMIM), 2025, IEEE.
F. Pervez, et al., “Wireless technologies for emergency response: A comprehensive review and some guidelines,” IEEE Access, vol. 6, pp. 71814–71838, 2018.
M.-F. R. Lee and T.-W. Chien, “Artificial intelligence and internet of things for robotic disaster response,” in Proc. 2020 Int. Conf. Adv. Robot. Intell. Syst. (ARIS), 2020, IEEE.
S. Ahmed, M. Rashid, F. Alam, and B. Fakhruddin, “A disaster response framework based on IoT and D2D communication under 5G network technology,” in Proc. 2019 29th Int. Telecommun. Netw. Appl. Conf. (ITNAC), 2019, IEEE.
M. Arslan, A.-M. Roxin, C. Cruz, and D. Ginhac, “A review on applications of big data for disaster management,” in Proc. 2017 13th Int. Conf. Signal-Image Technol. Internet-Based Syst. (SITIS), 2017, IEEE. DOI: https://doi.org/10.1109/SITIS.2017.67
Y. He, J. He, and N. Wen, “The challenges of IoT-based applications in high-risk environments, health and safety industries in the Industry 4.0 era using decision-making approach,” J. Innov. Knowl., vol. 8, no. 2, p. 100347, 2023.
F. Reegu, et al., “A reliable public safety framework for industrial internet of things (IIoT),” in Proc. 2020 Int. Conf. Radar, Antenna, Microw., Electron. Telecommun. (ICRAMET), 2020, IEEE.
J. Preston, et al., Emerging Threats and Technology Investigation: Industrial Internet of Things—Risk and Mitigation for Nuclear Infrastructure. Oak Ridge, TN, USA: Oak Ridge Y-12 Plant, 2022.
M. Thibaud, H. Chi, W. Zhou, and S. Piramuthu, “Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review,” Decis. Support Syst., vol. 108, pp. 79–95, 2018. DOI: https://doi.org/10.1016/j.dss.2018.02.005
S. Hua, “Internet of Things-driven safety and efficiency in high-risk environments: Challenges, applications, and future directions,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 5, 2025.
L. L. Dhirani, E. Armstrong, and T. Newe, “Industrial IoT, cyber threats, and standards landscape: Evaluation and roadmap,” Sensors, vol. 21, no. 11, p. 3901, 2021.
B. Chen, et al., “Edge computing in IoT-based manufacturing,” IEEE Commun. Mag., vol. 56, no. 9, pp. 103–109, 2018.
N. Hassan, et al., “The role of edge computing in internet of things,” IEEE Commun. Mag., vol. 56, no. 11, pp. 110–115, 2018.
C. Fan, C. Zhang, A. Yahja, and A. Mostafavi, “Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management,” Int. J. Inf. Manage., vol. 56, p. 102049, 2021.
J. Pan and J. McElhannon, “Future edge cloud and edge computing for internet of things applications,” IEEE Internet Things J., vol. 5, no. 1, pp. 439–449, 2017. DOI: https://doi.org/10.1109/JIOT.2017.2767608
P. Porambage, et al., “Survey on multi-access edge computing for internet of things realization,” IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 2961–2991, 2018.
G. Premsankar, M. Di Francesco, and T. Taleb, “Edge computing for the Internet of Things: A case study,” IEEE Internet Things J., vol. 5, no. 2, pp. 1275–1284, 2018. DOI: https://doi.org/10.1109/JIOT.2018.2805263
M. Aboualola, et al., “Edge technologies for disaster management: A survey of social media and artificial intelligence integration,” IEEE Access, vol. 11, pp. 73782–73802, 2023.
T. Khan, et al., “RESCUE: A resilient cloud based IoT system for emergency and disaster recovery,” in Proc. 2018 IEEE 20th Int. Conf. High Perform. Comput. Commun. (HPCC/SmartCity/DSS), 2018, IEEE.
H. S. Munawar, et al., “Disruptive technologies as a solution for disaster risk management: A review,” Sci. Total Environ., vol. 806, p. 151351, 2022.
F.-Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “An overview of big data opportunities, applications and tools,” in Proc. 2015 Intell. Syst. Comput. Vis. (ISCV), 2015, pp. 1–6. DOI: https://doi.org/10.1109/ISACV.2015.7105553
D. Emmanouil and D. Nikolaos, “Big data analytics in prevention, preparedness, response and recovery in crisis and disaster management,” in Proc. 18th Int. Conf. Circuits, Syst., Commun. Comput. (CSCC), 2015.
S. Rahman, L. Di, and M. Esraz-Ul-Zannat, “The role of big data in disaster management,” in Proc. Int. Conf. Disaster Risk Mitigation, 2017.
D. Sudharsan, et al., “Dynamic real time distributed sensor network based database management system using XML, JAVA and PHP technologies,” Int. J. Database Manage. Syst., vol. 4, no. 1, pp. 9–20, 2012. DOI: https://doi.org/10.5121/ijdms.2012.4102
V. D. Gowda, A. Sharma, K. Prasad, R. Saxena, T. Barua, and K. Mohiuddin, “Dynamic disaster management with real-time IoT data analysis and response,” in Proc. 2024 Int. Conf. Autom. Comput. (AUTOCOM), pp. 142–147, IEEE, 2024.
S. K. Abid, N. Sulaiman, S. W. Chan, U. Nazir, M. Abid, H. Han, A. Ariza-Montes, and A. Vega-Muñoz, “Toward an integrated disaster management approach: How artificial intelligence can boost disaster management,” Sustainability, vol. 13, no. 22, p. 12560, 2021.
X. Liu, “Open-source low-cost internet of things platform for buildings,” 2017.
D. Widodo, P. Kristalina, M. Z. S. Hadi, and A. D. Kurniawati, “Performance evaluation of docker containers for disaster management dashboard web application,” in Proc. 2023 Int. Electron. Symp. (IES), pp. 551–556, IEEE, 2023.
V. Nunavath and M. Goodwin, “The use of artificial intelligence in disaster management—A systematic literature review,” in Proc. 2019 Int. Conf. Inf. Commun. Technol. Disaster Manage. (ICT-DM), pp. 1–8, IEEE, 2019.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY