Wireless Sensor Networks (WSNs) are a technology that is becoming very popular for many applications, and environmental monitoring is one of its most important application areas. This technology solves the lack of flexibility of wired sensor installations and, at the same time, reduces the deployment costs. To demonstrate the advantages of WSN technology, for the last five years we have been deploying some prototypes in the Doñana Biological Reserve, which is an important protected area in Southern Spain. These prototypes not only evaluate the technology, but also solve some of the monitoring problems that have been raised by biologists working in Doñana. This paper presents a review of the work that has been developed during these five years. Here, we demonstrate the enormous potential of using machine learning in wireless sensor networks for environmental and animal monitoring because this approach increases the amount of useful information and reduces the effort that is required by biologists in an environmental monitoring task.

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In this paper we present a novel algorithm, implemented in a wireless sensor network environment, for the study of fire in a natural environment. This algorithm can offer two kinds of information: the risk of a fire and, in case of an active fire, the propagation direction of the flames and their velocity. This information is useful for fire rangers in order to obtain escape ways and determine strategies for putting off the fire. The evaluation of the proposal algorithm is performed using an ad-hoc simulator. The preliminary results obtained show that the proposed algorithm is a promising method that can be used to solve the most common problems related to fire control and detection.