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Special Issue Call for Papers

Internet of Things (IOT) for Disaster Management Systems

Submission deadline:
25 January 2020

Guest Editors:


The ‘Internet of Things’ (IoT) is a term for a system of interconnected devices, machines, and objects that can transfer data over a network without requiring human or computer interactions. In IoT, all of the things are connected to the internet to collect and send information, and to receive and act on that information. Objects and devices incorporate data through built-in sensors from different devices and relate analytics to share information to address specific needs. This information is used to make suggestions and to detect patterns and possible problems before they occur. A disaster is any event, natural or man-made, that has a negative impact on people and the environment. Disasters can cause great damage, destruction, loss and devastation, not only for human life but also to transport networks, infrastructure and the economy, such that it is essential to allocate resources to avoid disaster occurrence by developing warning systems and creating proper methods.

IoT cannot prevent an upcoming disaster, but it can help those involved to be as prepared as possible by creating an early-prediction and caution system. In case of unpredicted disaster situations, IoT can warn those who may be affected and help to broadcast emergency instructions whenever necessary, depending upon the criticality of the situation. These IoT sensors can also monitor humidity, temperature and weather patterns to predict any type of natural disaster and it can even identify potentially affected areas. In order to be prepared for critical happenings, data are collected from a variety of locations in a real-time process. Hence, the development of IoT for disaster management is a key step towards avoiding great loss in disaster events, by taking advantage of powerful sensing ability of the physical environment.

There has been increasing interest in implementing disaster-management systems by using IoT among researchers. This special issue will serve as an innovative and interesting platform for enriching and discussing the opportunities for finding better solutions for innovative disaster-management methods. This issue will also make a significant contribution to the development of appropriate IoT technology in the context of disaster management.

Topics of interest include but are not restricted to:

  • IoT-enabled early-warning and -prediction systems for coastal environments
  • Future advancements in the implementation of disaster-management systems
  • Opportunities and threats of IoT-enabled devices for disaster-response assessments
  • Design and implementation of disaster-management systems by using IoT-based interconnected networks
  • Role of IoT in collective action for dynamic disaster environments
  • A study on various protocols for IoT-based communication networks
  • Need of IoT in critical and emergency situations
  • Disruptive technologies for disaster-risk reduction and management
  • IoT-based emergency planning in natural disasters
  • IoT for post-disaster management

Submit your Manuscript

Geomatics, Natural Hazards and Risk is an Open Access, peer-reviewed journal.

Please read the Instructions for Authors to prepare your manuscript for submission.


Important Dates

Paper Submission Deadline: 25th January 2020

Author notification: 15th April 2020

Revised papers submission: 10th July 2020

Final Acceptance: 30th September 2020

Meet the Guest Editors

Dr. Stephanie Thomas

Faculty of Biology, Chemistry & Earth Sciences

Department of Biogeography

University of Bayreuth

Dr. Stephanie Thomas is Professor of Faculty of Biology in Department of Biogeography at University of Bayreuth. Her current research interest focuses on invasive disease vectors, mainly mosquitoes with tropical and subtropical origin. Thereby the spatial and temporal variability of distribution patterns of vector species and their associated diseases considering climate change and globalization as major drivers is paramount. Risk analysis involves modeling techniques and experimental approaches. She is also interested in the interconnections between climate change, biodiversity and health.

Google Scholar: https://scholar.google.com/citations?hl=en&user=9pO7DgMAAAAJ

Research Gate: https://www.researchgate.net/profile/Stephanie_Thomas4


Prof. Dr. Paul Burton

Professor of Data Science, Institute of Health and Society

Newcastle University

Dr. Paul Burton is Professor of Data Science for Health in the Institute of Health and Society, Newcastle University. Over his career his research has encompassed three broad themes Methods research in biostatistics and genetic epidemiology, applied research in genetic epidemiology and complex disease epidemiology, and Health Data Science. His current research program encompasses biostatistics, epidemiology and informatics - including their associated social and ethico-legal dimensions. Paul is also PI of 58-FORWARDS, a grant jointly awarded by MRC and Wellcome Trust (WT) that funds maintenance and enhancement of the infrastructure underpinning access to data and biosamples from the Biomedical Resource of the 1958 Birth Cohort.

Google Scholar: https://scholar.google.com/citations?user=xOPpAK4AAAAJ&hl=en

Research Gate: https://research.ncl.ac.uk/d2k/staff/professorpaulburton.html


Professor Dr. Andreas Mayr

Dept. of Medical Biometry, Informatics and Epidemiology

University of Bonn

Dr. Andreas Mayr is a Professor for Epidemiology at the University Bonn and head of WG Statistial Methods in Epidemiology. His research interests are Statistical boosting, GAMLSS (generalized additive models for location, scale and shape), Quantile regression, Prediction inference and prediction intervals. He has an overall citation of 1600 points. He has published over 90 research items.

Google Scholar: https://scholar.google.com/citations?hl=en&user=CiH3niAAAAAJ

Research Gate: https://www.researchgate.net/profile/Andreas_Mayr2