Digital Twin-driven Crisis mAnagement using aRtificial intelligEnce

Demo Description

We will present an application of a Digital Twin based framework in a crisis management and mitigation scenario taking place at a university campus and especially at the University of Athens – department of Informatics and Telecommunications.

In this scenario, network management plays a crucial role in ensuring efficient communication and coordination during emergency situations. The digital twin serves as a virtual representation of the university, enabling real-time monitoring and simulation of various crisis scenarios. The network infrastructure, powered by a mobile 5G network, seamlessly connects emergency responders, university staff, and other stakeholders involved in crisis management.

By leveraging the fast and reliable transmission capabilities of 5G, the network enables real-time information sharing, video feeds, and effective communication channels. This allows for timely decision-making and coordinated responses in critical situations. Within the digital twin environment, the network management system dynamically adapts to changing conditions, such as increased communication load during emergencies or unexpected obstacles. It optimizes resource allocation to ensure uninterrupted communication channels and efficient allocation of emergency resources. These adaptive mechanisms enhance the overall performance of crisis management operations within the university campus.

Moreover, the energy-saving mechanisms integrated into the system efficiently manage resources that are not actively utilized, further optimizing the network’s efficiency. This ensures that critical resources, such as power and bandwidth, are allocated to areas that require immediate attention during crisis events.

Scenario:

In a crisis management scenario, a fire breaks out in one of the campus buildings. Emergency responders are immediately alerted through the based framework. The Artificial Intelligence (AI) – driven network system swiftly coordinates communication channels, connecting firefighters, campus security, and relevant personnel.

Real-time updates and critical information are transmitted seamlessly via a reliable mobile 5G network. Within the digital twin environment, the network dynamically adapts to the crisis, allocating resources efficiently. Emergency responders receive high-quality information with low latency, enabling them to make informed decisions.

The digital twin’s energy-saving machine learning based mechanisms optimise resource allocation and conserve energy when not actively utilised. The crisis is managed effectively, ensuring the safety of students, staff, and the university community.

Learning Objectives:

1. Exploring the integration of a 3D digital twin.
2. Recognizing the importance of adaptive transmissions.
3. Analysing resource orchestration and energy-saving mechanisms.
4. Understanding the impact on safety and efficiency.

Level: Advanced

Prerequisites on audience: 

1. Having knowledge of the concept of smart cities, which involves the integration of technology and data to improve energy, sustainability, and infrastructure.
2. Familiarity with the concept of digital twins, which involves creating virtual
representations of physical systems to monitor, analyse, and optimise their
performance.
3. Basic knowledge of crisis management principles, procedures, and challenges faced in emergency situations.
4. Familiarity with network management concepts, such as resource allocation, dynamic adaptation, and efficient communication in complex scenarios.
5. A general understanding of the capabilities and benefits of 5G mobile networks, including fast and reliable transmission, low latency, and seamless communication.

Target audience:

1. Researchers and Academics
2. Industry Professionals
3. Engineers and Technologists
4. Urban Planners and Policy Makers

TAGS: #DigitalTwin, #AI, #Safety, #CrisisManagement, #RealTime, #5G