## 5 Emerging Challenges and Opportunities
### Mobile Sensing and Cloud-Enabled Decisions  
As smart phones become ever more present and cloud computing emerges as a dominant technology, crowdsourced data in connection with cloud computing may greatly facilitate the evacuation and rescue systems of the future. Thus, we next discuss some emerging opportunities and technologies from this perspective. _Augmented reality with crowdsourced data:_ mobile phones are considered as simple clients in many evacuation and rescue systems, due to their sensing ability and popularity. Combining crowdsourced data with virtual or augmented reality on smart phones raises some challenges in terms of visual navigation directions, accurate positioning and aggregation of crowdsourced information. Visual navigation directions can annotate the direction of motion on smartphones to guide people clearly. Accurate localization systems guarantee precise positioning of victims, which cannot be supported by the low-accuracy GSM positioning technology. Aggregation of crowdsourced information provides first responders and victims with the wisdom of crowds. Since people in a disaster are usually under stress and likely to be confused in [86], mobile phones guide users to the exit by using 3D building models augmented with visual evacuation directions based on the evacuees' current positions and environmental situations in the building. Two important technologies, localization of victims and visualization of 3D building, are addressed in the system. The localization of victims relies on an active RFID system, while the 3D visualization is based on a mobile 3D rendering engine. Instead of a virtual building model, _Augmented Reality (AR)_ guiding technologies could be preferred in emergencies, especially for people who are unfamiliar with the environments. Instead of costly RFID infrastructure, the work in [87] implements an infrastructure-less emergency evacuation system, which adopts built-in sensors on smartphones (e.g., accelerometers, cameras and digital compasses) and image-based localization technologies for flexibility and scalability purposes. The back-end server localises victims by matching image snapshots with well-known locations to provide them with uncrowded paths, where well-known locations could be crowdsourced labels. Since existing map providers (e.g., GoogleMaps) provide only roads or building shapes without enough information about the connection points between buildings and roads in the real world, in [88], a crowdsourced map which allows users to contribute evacuation routes between buildings and routes, so as to provide a more accurate environmental model for evacuation simulations, is suggested. Similarly, [89] provides volunteers with a crowdsourced map to tag information on incidents that need rescuers' attention. _Cloud-enabled decision systems_: as massive and multi-domain data need to be processed quickly in emergency response systems, cloud computing may be adopted to design more satisfactory systems with higher processing speed, larger storage memory, less risk, lower cost and higher capability for integration. In [90], computation tasks, including RFID-based positioning, calculation of evacuation paths and database management, are migrated to a back-end cloud platform. Considering urban-scale emergencies (e.g., earthquakes or tsunami disasters), [91] considers a cloud-enabled vehicular emergency response system to evacuate people from city. This system consists of three layers: (1) cloud infrastructure layer; (2) intelligence layer; and (3) system interface. The cloud infrastructure layer adopts a Xen hypervisor to provide a parallel and distributed architecture. The intelligence layer is deployed computational models and algorithms to process multi-domain data and make decisions on evacuationroutes based on density and velocity of vehicles. The system interface acquires data from the vehicular networks via mobile phones or roadside communication units.