Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications

Topic Information

Dear Colleagues,

Several types of disasters are increasing in frequency and severity in the modern world, with significant impacts on human lives and the economy. Disaster management planning is structured around the disaster management cycle model with four stages, namely, mitigation, preparedness, response, and recovery. Technological developments in the fields of IoT, edge computing, digital twins, machine learning, computer vision, embedded systems, emergency communications, and integrated sensors and platforms, can play a significant role in hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness activities, systems, and processes which enable individuals, communities, governments, businesses, and others to take timely action to reduce disaster risks in advance of hazardous events. This topic invites the submission of manuscripts that present both research in both academia and industry associated with disaster and crisis management applications.

Dr. Evangelos Maltezos
Dr. Eleftherios Ouzounoglou
Dr. Panagiotis Michalis
Dr. Angelos Amditis
Dr. Stefanos Vrochidis
Prof. Dr. Norman Kerle
Dr. Christos Ntanos
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600
Information information	2.4	6.9	2010	16.4 Days	CHF 1600
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Drones Information Sensors

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

22 pages, 6891 KiB  

Open AccessEditor’s ChoiceArticle

Transponder: Support for Localizing Distressed People through a Flying Drone Network

by Antonello Calabrò and Eda Marchetti

Drones 2024, 8(9), 465; https://doi.org/10.3390/drones8090465 - 6 Sep 2024

Cited by 2 | Viewed by 1187

Abstract

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and [...] Read more.

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios. Full article

(This article belongs to the Topic Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications)

►▼ Show Figures

Figure 1

21 pages, 11491 KiB  

Open AccessArticle

FIWARE-Compatible Smart Data Models for Satellite Imagery and Flood Risk Assessment to Enhance Data Management

by Ioannis-Omiros Kouloglou, Gerasimos Antzoulatos, Georgios Vosinakis, Francesca Lombardo, Alberto Abella, Marios Bakratsas, Anastasia Moumtzidou, Evangelos Maltezos, Ilias Gialampoukidis, Eleftherios Ouzounoglou, Stefanos Vrochidis, Angelos Amditis, Ioannis Kompatsiaris and Michele Ferri

Information 2024, 15(5), 257; https://doi.org/10.3390/info15050257 - 2 May 2024

Cited by 3 | Viewed by 2077

Abstract

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector are leading to a shift to a Water-Smart Society. New challenges have emerged in terms [...] Read more.

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonization and smart modeling of the data to foster advanced AI analytical processes, which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the satellite imagery data and the flood risk assessment processes. The utilization of those models reinforces the fusion and homogenization of diverse information and data, facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework is developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customized and applicable easily to support the water data management processes effectively. Full article

(This article belongs to the Topic Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications)

►▼ Show Figures

Figure 1

23 pages, 1376 KiB  

Open AccessArticle

A Semantic Framework for Decision Making in Forest Fire Emergencies

by Panagiota Masa, Spyridon Kintzios, Zoe Vasileiou, Georgios Meditskos, Stefanos Vrochidis and Ioannis Kompatsiaris

Appl. Sci. 2023, 13(16), 9065; https://doi.org/10.3390/app13169065 - 8 Aug 2023

Cited by 3 | Viewed by 2346

Abstract

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the [...] Read more.

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the available information in order to extract useful insights and knowledge to facilitate advanced whereas and support human operators and authorities in a real operational scenario is a challenge. In this work, we present ONTO-SAFE, an ontology-based framework for wildfire events, adopting Semantic Web technologies for data integration and infusion of domain and background knowledge. More specifically, the framework creates a unified representation of the available assets, taking into account data generated from different sources, such as sensors, weather forecasts, earth observations, etc. To this end, previously existing ontologies and standards are used, such as Empathi and EmergencyFire ontology, to provide the conceptual model and the necessary level of abstraction in the form of interconnected knowledge graphs to satisfy the modeling requirements. On top of the generated knowledge graphs, a declarative framework extracts facts and higher-level inferred knowledge from asserted data to support users in decision making. In addition, the framework supports the generation of recommendations, such as sharing important wildfire information with citizens and professionals, that can be adjusted based on user-defined factors and the current disaster risk management phase. Full article

(This article belongs to the Topic Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3