Emergency Management

Description of the DVC THEME

Focused on Emergency Management, this theme intends to provide platforms, services, or applications capable of managing risks for communities, the environment, and infrastructure. 

Such platforms and services are the core business of Emergency Services, but every individual and organization has a role to play. 

In addition, those solutions shall use IoT sensors and other integrations to increase the ability to collect data and make informed decisions. Moreover, they should help the main emergency stakeholders to be better prepared, respond more efficiently, and send vital information to those who need it (and learn from such information in similar cases). To achieve this, the solutions must efficiently collect and process heterogeneous data (i.e. different sources, different domains, etc) to provide smooth real-time reporting, critical updates, and actionable intelligence, as seconds can be the difference between a life-or-death disaster.

Furthermore, since the emergency management cycle largely depends on latency and rapid decision-making, edge/fog services must be developed and implemented to adapt the network to the required real-time response. 

Thus, the theme is focused on the computing continuum to overcome the complex decision-making process throughout the emergency cycle. In other words, where to process the data (edge vs core), when to store it, and to whom shall it be shared are important aspects of the final solution and are a common theme in the computing continuum. 

Finally, the developed solutions shall allow integration with external services to ensure that SMEs, data providers, and start-ups can deploy and test their solutions to improve the capability of responding in a disaster.

Expected global results:

To create a data value chain that allows:

    1. Exchanging sensitive information (emergency status, people allocated, etc) between different stakeholders of the emergency cycle;
    2. Increase the system’s capability to respond to a disaster, by providing the necessary information to the stakeholders and increasing the resilience of the network, namely with pre-processed historical aggregated data;
    3. Improve the capability to extract and analyse sensitive information and use it for emergency management, to enhance the preparedness and decision making process, both at the edge and in the cloud;
    4. Explore and expand the computing capabilities over a mesh network;
    5. Ensure actionable decisions during a crisis;
    6. Provide accountability information to trace both ML-models versions and data used to train them;
    7. Digitise the emergency response life cycle by using the computing continuum capabilities over the emergency response;
    8. Reinforce machine learning models with information processed at the edge.