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Abstract:In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCAD TM X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them.

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.

The discrepancy between the stiffness of commercially pure titanium and cortical bone tissue compromises its success as a biomaterial. The use of porous titanium has been widely studied, however, it is still challenging to obtain materials able to replicate the porous structure of the bones (content, size, morphology and distribution). In this work, the freeze-casting technique is used to manufacture cylinders with elongated porosity, using a home-made and economical device. The relationship between the processing parameters (diameter and material of the mold, temperature gradient), microstructural features and mechanical properties is established and discussed, in terms of ensuring biomechanical and biofunctional balance. The cylinders have a gradient porosity suitable for use in dentistry, presenting higher Young’s modulus at the bottom, near the cold spot and, therefore better mechanical resistance (it would be in contact with a prosthetic crown), while the opposite side, the hot spot, has bigger, elongated pores and walls.

The study presents a novel computational intelligence algorithm designed to optimise energy consumption in an environmental monitoring process: specifically, water level measurements in flooded areas. This algorithm aims to obtain a tradeoff between accuracy and power consumption. The implementation constitutes a data aggregation and fusion in itself. A harsh environment can make the direct measurement of flood levels a difficult task. This study proposes a flood level estimation, inferred through the measurement of other common environmental variables. The benefit of this algorithm is tested both with simulations and real experiments conducted in Doñana, a national park in southern Spain where flood level measurements have traditionally been done manually. the terrain. Therefore it is important to design robust software and hardware that can be adapted to any incident. † Flexibility: The network must be able to add, move or remove nodes to meet the application requirements. The network must automatically detect the changes, organising the communications in consequence. One of the most important constraints for this application concerns energy consumption. The batteries that provide power supply to these devices have, in general, a short life. To overcome this problem, this study presents an aggregation and data-fusion technique, that allows us to reduce the network's power consumption. The parameter considered in this study is the water level in flooded zones. We studied it using a simulator to determine the advantages of local processing and data fusion to reduce power consumption. The data fusion is based on a local Self-Organised Map (SOM) [5] distributed in each node of the WSN. The results obtained by simulation have been compared with the real deployment of a WSN. The experimental results in real scenarios demonstrate the performance of the system and validate the results obtained by simulation. Since the start of the installation we have collected partial information about the mounted sensors and their reliability. These results are discussed in Section 6. The rest of this study is organised as follows: Section 2 describes the application scenario of the proposed method; Section 3 summarises the state-of-the-art about aggregation and data fusion in WSN; Section 4 describes the proposed aggregation methods with outcome of this method

This paper proposes a novel and autonomous weighing system for wild animals. It allows evaluating changes in the body weight of animals in their natural environment without causing stress. The proposed system comprises a smart scale designed to estimate individual body weights and their temporal evolution in a bird colony. The system is based on computational intelligence, and offers valuable large amount of data to evaluate the relationship between long-term changes in the behavior of individuals and global change. The real deployment of this system has been for monitoring a breeding colony of lesser kestrels (Falco naumanni) in southern Spain. The results show that it is possible to monitor individual weight changes during the breeding season and to compare the weight evolution in males and females.

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.

SUMMARYCurrently, wireless sensor networks (WSNs) are formed by devices with limited resources and limited power energy availability. Thanks to their cost effectiveness, flexibility, and ease of deployment, wireless sensor networks have been applied to many scenarios such as industrial, civil, and military applications. For many applications, security is a primary issue, but this produces an extra energy cost. Thus, in real applications, a trade-off is required between the security level and energy consumption. This paper evaluates different security schemes applied to human tracking applications, based on a real-case scenario.