# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 3 Challenges for Risk Assessment and Mission Control in SAR Operations in Post-Disaster Scenarios  
When a natural or human disaster occurs, the main objective is to rescue as many victims as possible in the shortest possible time. To this aim, the rescue team needs to (1) locate and identify victims, (2) assess the victims' injuries and (3) assess the damages and comprehend the remaining risks to prioritize rescue operations. All these actions are complementary and require a different part of the data collected in the area. On the data coming from sensors, cameras and other devices, image recognition, AI-powered decision-making, path planning and other technological solutions can be implemented to support the rescue teams in enhancing their situational awareness. Today, only part  
Figure 1: The ROS ecosystem is composed of multiple elements: (i) plumbing: a message-passing system for communication; (ii) tools: a set of development tools to accelerate and support application development; (iii) capabilities: drivers, algorithms, user interfaces as building blocks for applications; (iv) community: a large, diverse, and global community of students, hobbyists and multinational corporations and government agencies. of the available data can be collected, and robots, although a great support, are not fully autonomous and just act as relays to the rescuers. The main technical challenges are linked to the heterogeneity of devices and strict time requirements. Data should be filtered and processed at different levels of the continuum to guarantee short delays while maintaining full knowledge of the situation. Devices are heterogeneous in terms of CPU, memory, sensors and energy capacities. Some of the hardware and software components are very specific to the situation (use-case specific), whereas others are common to multiple scenarios. Different complementary applications can be run on top of the same devices but exploit different sets of data, potentially incomplete. The network is dynamic because of link fluctuations, the energy depletion of devices and device mobility (which can also be exploited when controllable). The high-level goal for this scenario is to enhance situational awareness for first responders. Sensor data fusion built on ROS can help provide precise 3D representations of emergency scenarios in real time, integrating the inputs from multiple sensors, pieces of equipment and actors. Furthermore, collecting and visually presenting aggregated and processed data from heterogeneous devices and prioritizing selected information based on the scenario is an additional objective. All the information that is being collected should improve the efficiency of decision-making and responses and increase safety and coordination. Robotic platforms have features that are highly appreciated by first responders, such as the possibility to generate 3D maps of a disaster scene in a short time. Open-source technologies (i.e., ROS) offer the tools to aggregate sensor data from different coordinate frameworks. To achieve this, precise localization and mapping solutions in disaster scenarios are needed, together with advanced sensor data fusion algorithms. The envisaged real-time situation awareness is only possible through substantial research advancement with respect to the state of the art in localization, mapping, and cooperative perception in emergency environments. The ability to provide information from a single specialized device (e.g., drone streaming) has been demonstrated, whereas correctly integrating multiple heterogeneous moving data sources with imprecise localization in real-time is still an open challenge. We can summarize the main technical requirements and challenges as follows:  
1. **Dynamic multi-robot mapping and fleet management**: the coordination, monitoring and optimization of the task allocation for mobile robots that work together in building a map of unknown environments;
2.