Secure Mobile Notifications of Civilians in Case of a Disaster

Roßnagel, Heiko; Scherner, Tobias

doi:10.1007/11909033_4

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…This further reduces panic. For evacuation of people, Rossnagel and Scherner [105] presented a global system for mobile communications (GSM) alarm device to be deployed in the nearby area for early warnings. This device collects its data from the weather office, but this does not provide the residents with a quick way for evacuation.…”

Section: Applications Of Machine Learning Models In Crowd Evacuationmentioning

confidence: 99%

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Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

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Section: Applications Of Machine Learning Models In Crowd Evacuationmentioning

confidence: 99%

“…Different from [105] , Anzengruber et al [108] have proposed a smartphone app that notifies people about the evacuation routes. They have used two parameters to evaluate the location of the user and proximity-relations using Bluetooth.…”

Section: Applications Of Machine Learning Models In Crowd Evacuationmentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Fritsch and Scherner (2005) proposed a system in which early warning functionalities, enhanced by privacy-preserving mechanisms, are described. Based on this work, a detailed analysis how authenticity, integrity and non-repudiation can be provided by using electronic signatures has been provided in Roßnagel and Scherner (2006).…”

Section: System Requirementsmentioning

confidence: 99%

A Method for the Management of Service Innovation Projects in Mature Organizations

Schindlholzer

Uebernickel

Brenner

2011

International Journal of Service Science, Management, Engineering, and Technology

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The ability to design innovative services is an important capability for organizations in the 21st century. Although innovation is the fundamental force to create a sustainable business, many organizations, especially mature organizations, struggle to develop innovative services. This paper offers a method for managing service innovation projects in mature organizations. The method is described using the elements of method engineering. Its relevance is evaluated through an exploratory case study at the intersection of business and IT, focusing on a German financial services provider that sought to develop new IT-based service innovations. Information technology plays a major role as an enabler for a broad range of innovative services, and IT organizations are in a unique position to design services in collaboration with business units to address evolving customer requirements. The key finding of this case study is that while processes, methods, and tools are important for managing service innovation projects, socio-technical aspects such as context, environment, team management, and project setup also are essential for the successful design of innovative services. The current literature provides rudimentary guidance in these areas, yet a thorough description of these factors and their integration into a complete method has not yet been documented.

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Introducing Sim-Based Security Tokens as Enabling Technology for Mobile Real-Time Services

Roßnagel

Muntermann

2009

Identity and Privacy in the Internet Age

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Secure Mobile Notifications of Civilians in Case of a Disaster

Cited by 7 publications

References 6 publications

Disaster and Pandemic Management Using Machine Learning: A Survey

Disaster and Pandemic Management Using Machine Learning: A Survey

A Method for the Management of Service Innovation Projects in Mature Organizations

Introducing Sim-Based Security Tokens as Enabling Technology for Mobile Real-Time Services

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