## 3 General Overview  
Most existing works rely on a preexisting WSN deployed in the building. This assumption is not realistic in our approach. Many buildings do not have WSN deployment. In others, fire could have damaged the WSN. In our problem, firefighters deploy the nodes when they enter the building. Besides practical applicability, another advantage of this approach is that the WSN deployment can be adapted to each case, _i.e_., increasing the node density in order to enhance the monitoring of areas of interest, e.g., with victims of with flammable materials. Rapid deployment also involves some constraints. The previous information assumed by the system should be minimized and the methods should have high adaptability to changing conditions. The delay until the WSN is operative after deployment is also an issue in these systems. In the proposed system the WSN is comprised of static nodes and mobile nodes carried by the firefighters. When the fire brigade arrives at the fire, using a map of the building they decide the deployment of static nodes. Then, the nodes are deployed by the firefighters. It is not the objective of this paper to analyze the node deployment. It could be done, for instance, using the method described in [17]. Our paper focuses on fire monitoring, firefighter localization and escape path computation. Although deployment itself is left out of the paper, we deal with the aforementioned constraints of rapid deployment such as lack of previous information and need for high adaptability and low delays. Our system requires only two reasonable assumptions. The Base Station needs to know the building map, _i.e_., a representation of the configuration of rooms and hallways. Each static node needs to know the location in the map where it has been deployed. None of them is a significant constraint for applicability. Building map databases are often available to emergency brigades. In addition, as soon as the deployment for a certain fire has been decided, nodes can be informed about their locations using simple configuration packets. Static nodes provide a communication backbone with the Base Station through multi-hop routes computed dynamically to take into account the changing conditions. All nodes are equipped with suitable fire and smoke sensors. Measurements from static nodes are used for fire monitoring. Measurements from mobile nodes are used to monitor the conditions each firefighter is currently facing. The readings from all nodes are transmitted to the Base Station, typically in the fire truck, where measurements are visualized and registered. The proposed system includes the following main functionalities:  
* _Fire monitoring_. Sensor measurements are periodically gathered from static nodes and transmitted to the Base Station using multi-hop routes. * _Firefighter monitoring_. The objective is to gather and make available at the Base Station updated measurements of each firefighter including its location and the conditions the firefighter is facing. Location is estimated using a robust localization method that employs RSSI-range models dynamically trained to cope with the peculiarities of the environment. As described in Section 4.2.1, the training and application stages of the localization method are applied simultaneously, resulting in very short delays and high adaptability. The proposed method provided localization errors around 1.4 m in the indoor experiments carried out. * _Escape path computation_. The objective is to determine and make available to firefighters safe paths to desired locations, such as escape paths or paths to rooms with potential victims. The method adopts a guiding approach in which the path information is distributed in the WSN. Each static node keeps the information to guide the firefighters to the next adjacent room in the safe escape path. The Base Station uses optimization graph-based tools to periodically compute minimal cost paths using updated fire measurements. The new guiding information is broadcasted only if it represents a significant improvement.