# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 2 Related Work
### Situation Awareness and Perception with Mobile Robots  
Mobile robot teams with heterogeneous sensors have the potential of scaling up first responders' situational awareness. Being able to cover wide areas, these are an important asset in the response to catastrophic incidents, such as wildfires, urban fires, landslides, and earthquakes. In their essence, robot teams make possible two key dimensions of situation awareness: space distribution and time distribution [54]. Although the former means the possibility of multiple robots perceiving simultaneously different (far) locations of the area of interest, the latter arises from the fact that multiple robots, possibly endowed with complementary sensor modalities, can visit a location at different instant times, thus coping with environments' evolution over time, i.e., with dynamic environments. Together with space and time distribution, robot teams also potentially provide efficiency, reliability, robustness and specialization [55]; efficiency because they allow building in fewer time models of wide areas, which is of utmost importance to provide situational awareness within response missions to catastrophic scenarios; reliability and robustness because the failure of individual robots in the team does not necessarily compromise the overall mission success; specialization because robots have complementary, i.e., heterogeneous, sensory and mobility capabilities, thus increasing the team's total utility. Although distributed robot teams potentially allow for persistent, long-term perception in wide areas, i.e., they allow for cooperative perception, fulfilling this potential requires solving two fundamental scientific sub-problems [56]: (i) data integration for building and updating a consistent unified view of the situation, eventually over a large time span; (ii) multi-robot coordination to optimize the information gain in active perception [57; 58]. The former sub-problem deals with space and time distribution and involves data fusion, i.e., merging partial perceptual models of individual robots into a globally consistent perceptual model, so that robots can update percepts in a spot previously visited by other robotic teammates. The latter sub-problem involves the use of partial information contained in the global perceptual model to decide where either a robot or the robot team should go next to acquire novel or more recent data to augment or update the current perceptual model, thus closing the loop between sensing and actuation. In order to optimize the collective performance and take full advantage of space distribution provided by the multi-robot system, active perception requires action coordination among robots, which usually is based on sharing some coordination data among robots (e.g., state information). In both sub-problems, devising distributed solutions that do not rely on a central point of failure [55] is a key requirement to operate in the wild, e.g., in forestry environments or in large urban areas, so that adequate resiliency and robustness are attained in harsh operational conditions, including individual robots' hardware failures and communication outages. Cooperative perception has been studied in specific testbeds [55] or several robotics application domains, such as long-term security and care services in man-made environments [57], monitoring environmental properties [58; 59; 60; 61; 62; 63], the atmospheric dispersion of pollutants monitoring [64], precision agriculture [65; 66] or forest fire detection and monitoring [67; 68]. However, the research problem has been only partially and sparsely solved (i.e., usually tackling only a specific dimension of cooperative perception), and it is still essentially an open research problem.