Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness

Abstract

To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures

“…Several studies have explored the conceptual framework and architectural design of EMSs in smart cities, aiming to develop comprehensive and effective systems for emergency detection and response. For example, a novel EMS architecture has been proposed, integrating heterogeneous sensor networks and addressing data fusion techniques to enhance situational awareness and decision-making in emergency scenarios [21][22][23]. Another important perspective of EMSs is their distributed implementation, leveraging advanced communication technologies, such as Internet of Things (IoT) and cloud computing, into the EMS infrastructure to improve responsiveness and scalability [16,24,25].…”