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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. [ABSTRACT FROM AUTHOR]

An introduction to the special issue of "SIMULATION: Transactions of The Society for Modeling and Simulation International" is presented, which include articles on gang scheduling, reconstruction of arrival processes to discrete queueing systems and a simulation system to study the execution dynamics of distributed computing workflows.

Abstract: Virtual world applications allow users to interact within a simulated world. Network responsiveness and reliability contribute to the user experience, thus being able to model and reproduce certain network scenarios is a key issue to assure proper user experience and for being able to provide an estimation of the required network resources. The present study aims to model the client network traffic for the virtual world application Open Wonderland as the basis to tools for evaluating its network requirements. A micro scale modelling was performed, studying the outgoing network traffic from a black box approach that omits the details of traffic generation of the subcomponents and focuses on their overall combined traffic. The model obtained provides high goodness of fit for audio and object synchronisation traffic, reflected in a Pearson correlation coefficient close to its maximum value and low deviation figures measured by Root Mean Square Deviation. [Copyright &y& Elsevier]