## 2 Related Work
Finally, according to the requirements abovementioned and the characteristics of wireless sensor networks, the authors present several research challenges from the point of view of new protocols, hardware and software for WSNs. FireNet architecture is considered to be very useful in fire rescue processes. The Forest-fires Surveillance System (FFSS) has been developed to survey the mountains of South Korea [11]. [PERSON] _et al._ propose architecture composed of WSNs, a transceiver, middleware and a Web-application. The nodes of this network gather measurements of temperature, humidity and illumination from the environment. These data are concentrated in one node of the WSN called sink-node. This node sends the data to the transceiver (gateway) connected to Internet. Then, a middleware program determines the forest-fire risk-level by a formula from the Forestry Office. If a fire is detected, FFSS automatically activates an alarm to facilitate an early extinguishing of the fire. In this work, the nodes use TinyOs as an operating system. Besides, the WSN use a minimum cost path forwarding (MCF) to send their data to a sink-node. [PERSON] and [PERSON] [12] presented a WSN for forest fire detection based on the FireWeather Index (FWI) System, which is one of the most comprehensive forest fire danger rating systems in North America. This system determines the risk of propagation of a fire according to several index parameters. So, weather data is collected by the sensor nodes and it is analyzed in this work to calculate these indexes. Another aspect analyzed is the number of measurements taken from different sensors to minimize error estimation. They present and simulate a distributed algorithm to solve this problem. Finally, the authors compare their algorithm against others in the literature and they conclude that the proposed algorithm extends the network lifetime and can provide higher detection accuracy in some areas. The objective of FireWxNet [13] is to determine the behavior of fire rather than its detection. It consists of a WSN that is used to measure weather conditions around an active fire. Webcams are used to get visual data of burned area and a base station which is capable of providing long distance communication. Every half an hour, the system measures temperature, relative humidity, wind speed and direction. In contrast, cameras provide images continuously about the current state of the active fire. The developed system uses five, long-distance wireless links, three sensor networks, and two web-cameras. The results of the system are very good and they show that it is very useful to analyze fire behavior. To conclude, [PERSON] _et al_. have presented some papers about sensor networks for fire fighting. In one of them they propose a simulation environment called Equipment Destined for Orientation and Security (EIDOS) [14]. This platform analyzes and combines the geographical information of the area (topography, combustible...) and the data sensed by network nodes (temperature, humidity, wind direction and speed) to create a model of the fire. All these data are sent directly to the firefighters' handheld devices to help them with the forest fire fighting. This paper describes and simulates the proposed system, but it is not implemented in a real environment. The same authors proposed a wireless sensor network to gather environment data in real time [15]. The difference with the other papers is that these data are sent from the wireless sensor network to a base station and they are used to feedback a fire simulator. The approximations calculated by the simulator are more precise and they can be used to compute better predictions about the fire evolution and its behavior. Systems based on satellite images are not widely used because they do not provide real time fire detection and they are high cost. Nowadays, wireless sensor networks are fashionable in fire-fighting, but although it is the technology most used to detect fires, there are very few implementations published in the literature.