UAVFog-Assisted Data-Driven Disaster Response: Architecture, Use Case, and Challenges

2020 ◽  
pp. 591-606
Author(s):  
Xianglin Wei ◽  
Li Li ◽  
Chaogang Tang ◽  
Suresh Subramaniam
Keyword(s):  
Disaster Response ◽  
Data Driven ◽  
Use Case

scholarly journals Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 235
Author(s):  
Paulo Garcia ◽  
Francine Darroch ◽  
Leah West ◽  
Lauren BrooksCleator
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Issues ◽  
Social Computing ◽  
Social Systems ◽  
Human Interaction ◽  
Data Driven ◽  
Vulnerable Groups ◽  
Use Case ◽  
Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

Joint service-function deployment and task scheduling in UAVFog-assisted data-driven disaster response architecture

2021 ◽  
Author(s):  
Xianglin Wei ◽  
Li Li ◽  
Lingfeng Cai ◽  
Chaogang Tang ◽  
Suresh Subramaniam
Keyword(s):  
Task Scheduling ◽  
Disaster Response ◽  
Data Driven ◽  
Service Function ◽  
And Task

scholarly journals Rapid Multi-Dimensional Impact Assessment of Floods

2020 ◽  
Vol 12 (10) ◽  
pp. 4246 ◽  
Author(s):  
David Pastor-Escuredo ◽  
Yolanda Torres ◽  
María Martínez-Torres ◽  
Pedro J. Zufiria
Keyword(s):  
Social Media ◽  
Impact Assessment ◽  
Rural Areas ◽  
Disaster Response ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Sensor Data ◽  
Mobile Phone Data ◽  
Aggregated Data ◽  
The Impact

Natural disasters affect hundreds of millions of people worldwide every year. The impact assessment of a disaster is key to improve the response and mitigate how a natural hazard turns into a social disaster. An actionable quantification of impact must be integratively multi-dimensional. We propose a rapid impact assessment framework that comprises detailed geographical and temporal landmarks as well as the potential socio-economic magnitude of the disaster based on heterogeneous data sources: Environment sensor data, social media, remote sensing, digital topography, and mobile phone data. As dynamics of floods greatly vary depending on their causes, the framework may support different phases of decision-making during the disaster management cycle. To evaluate its usability and scope, we explored four flooding cases with variable conditions. The results show that social media proxies provide a robust identification with daily granularity even when rainfall detectors fail. The detection also provides information of the magnitude of the flood, which is potentially useful for planning. Network analysis was applied to the social media to extract patterns of social effects after the flood. This analysis showed significant variability in the obtained proxies, which encourages the scaling of schemes to comparatively characterize patterns across many floods with different contexts and cultural factors. This framework is presented as a module of a larger data-driven system designed to be the basis for responsive and more resilient systems in urban and rural areas. The impact-driven approach presented may facilitate public–private collaboration and data sharing by providing real-time evidence with aggregated data to support the requests of private data with higher granularity, which is the current most important limitation in implementing fully data-driven systems for disaster response from both local and international actors.

Leveraging Data Analytics and Scalable Information Technology Tools to Deliver a Culture of Care during an Emergent Natural Disaster

2020 ◽  
pp. 1-5
Author(s):  
Marie Lynn Miranda ◽  
Max Grossman ◽  
Joshua L. Tootoo ◽  
Claire Osgood ◽  
Klara Jelinkova
Keyword(s):  
Information Technology ◽  
Natural Disaster ◽  
Disaster Response ◽  
Data Analytics ◽  
Data Driven ◽  
Community Members ◽  
Culture Of Care ◽  
Successful Communication ◽  
Data Analyses ◽  
Technology Tools

Abstract Rice University’s Culture of Care represents a commitment to ensuring that all are treated with respect, compassion, and deep care. Rice leveraged information technology (IT) to deliver its Culture of Care, in responding to Hurricane Harvey. IT tools were used to gather key information on Rice’s over 12000 community members. These data were fused with structured university data, enabling data-driven disaster response, with actionable information pushed to local managers. Our successful communication and response programs were all driven by the data analyses.

scholarly journals Design of a joint research data platform: A use case for severity assessment

2019 ◽  
Vol 54 (1) ◽  
pp. 33-39
Author(s):  
Steven R Talbot ◽  
Stefan Bruch ◽  
Fabian Kießling ◽  
Michael Marschollek ◽  
Branko Jandric ◽  
...  
Keyword(s):  
Human Factor ◽  
Data Driven ◽  
Use Case ◽  
Severity Assessment ◽  
Joint Research ◽  
Web Based ◽  
Specific Content ◽  
Data Platform ◽  
Basic Understanding ◽  
Minimal Effort

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

Comparison of Data-Driven Models for Cleaning eHealth Sensor Data: Use Case on ECG Signal

2020 ◽  
Vol 114 (2) ◽  
pp. 1501-1517
Author(s):  
Ana Koren ◽  
Marko Jurčević ◽  
Ramjee Prasad
Keyword(s):  
Data Use ◽  
Data Driven ◽  
Sensor Data ◽  
Ecg Signal ◽  
Use Case

scholarly journals Data-driven Auditory Contrast Enhancement for Everyday Sounds and Sonifications

2019 ◽  
Author(s):  
Thomas Hermann ◽  
Marian Weger
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
Contrast Enhancement ◽  
Medical Data ◽  
Data Driven ◽  
Group Differences ◽  
Use Case ◽  
Frequent Use ◽  
Group Similarity

We introduce Auditory Contrast Enhancement (ACE) as a technique to enhance sounds at hand of a given collection of sound or sonification examples that belong to different classes, such as sounds of machines with and without a certain malfunction, or medical data sonifications for different pathologies/conditions. A frequent use case in inductive data mining is the discovery of patterns in which such groups can be discerned, to guide subsequent paths for modelling and feature extraction. ACE provides researchers with a set of methods to render focussed auditory perspectives that accentuate inter-group differences and in turn also enhance the intra-group similarity, i.e. it warps sounds so that our human built-in metrics for assessing differences between sounds is better aligned to systematic differences between sounds belonging to different classes. We unfold and detail the concept along three different lines: temporal, spectral and spectrotemporal auditory contrast enhancement and we demonstrate their performance at hand of given sound and sonification collections.

scholarly journals Experience, Data-Driven and Artificial Intelligence in Social (Fire) and Chemical Technology

2019 ◽  
pp. 45-46
Author(s):  
Takashiro Akitsu ◽  
Yuika Onami
Keyword(s):  
Artificial Intelligence ◽  
Fire Technology ◽  
Disaster Response ◽  
Data Driven ◽  
Disaster Prevention ◽  
Emergency Situations ◽  
Prevention Activities ◽  
In Fire ◽  
Post Disaster ◽  
Disaster Prediction

“Realization of society 5.0 in fire and disaster prevention activities” is one of intensive goals of Japanese government developing fire technology [1]. Improvement of new equipment and materials for disaster response utilizing AI and ICT should be developed according to social requirements. Efforts to predict earthquake, pour, flood, etc. through AI analysis of data collected from past disasters must continue. In parallel with such elaboration of disaster prediction, it is necessary to proceed with preparations for prompt and accurate provision of disaster information during emergency situations and support for rebuilding lives post disaster.

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