D. F. Larios

Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring

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 trade-off 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 Donana, a national park in southern Spain where flood level measurements have traditionally been done manually.

mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks

Abstract Knowledge of the battery lifetime of the wireless sensor network is important for many situations, such as in evaluation of the location of nodes or the estimation of the connectivity, along time, between devices. However, experimental evaluation is a very time-consuming task. It depends on many factors, such as the use of the radio transceiver or the distance between nodes. Simulations reduce considerably this time. They allow the evaluation of the network behavior before its deployment. This article presents a simulation tool which helps developers to obtain information about battery state. This simulator extends the well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications using an accurate model of the battery consumption and its relation to the radio power transmission. Although an specific indoor scenario is used in testing of simulation, the simulator is not limited to this environment. It is possible to work in outdoor scenarios too. Experimental results validate the proposed model.

Five years of designing wireless sensor networks in the Doñana Biological Reserve (Spain): an applications approach.

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.

Applying case based reasoning for prioritizing areas of business management

Determining the importance of different management areas in a company provides guidance about the needs of increasing the analysis and actions focuses in particular topic. To do it, it is necessary to decompose the management in a coherent set of specific management areas and provide a way that allows the company to determine the importance of these areas for them. This paper presents a novel system that guides companies to obtain a classification of important management areas for them. It is focused on the use of a case based reasoning system because the variability and the evolution of companies as time passes requires using techniques with learning capabilities. The proposed system provides an automatic self-assessment system that provides companies an ordered list of their most important management areas. This system was implemented a year ago for the evaluation of Spanish companies. Currently, it is in production providing relevant information about the management areas of these companies.

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

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.

Securing a wireless sensor network for human tracking: a review of solutions

Currently, 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. Copyright

Locating sensors with fuzzy logic algorithms

In a system formed by hundreds of sensors deployed in a huge area it is important to know the position where every sensor is.

An automatic weighting system for wild animals based in an artificial neural network: how to weigh wild animals without causing stress

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.

Localization Method for Low-power Wireless Sensor Networks

Context awareness is an important issue in ambientintelligence to anticipate the desire of the user and,in consequence, to adapt the system. In context awareness,localization is very important to enable a responsive environmentfor the users.Focusing on this issue, this paper presents a localizationsystem based on the use of Wireless Sensor Networksdevices. In contrast to a traditional RFID, these devices offerthe possibility of a collaborative sensing and processing ofenvironmental information.The proposed system is a range-free localization algorithmthat uses fuzzy inference to process the RSSI measurementand to estimate the position of mobile devices. The maingoal of the algorithm is to reduce the power consumptionand the cost of the devices, especially for the mobiles ones,maintaining the accuracy of the inferred position.

A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks

The study of forest fires has been traditionally considered as an important application due to the inherent danger that this entails. This phenomenon takes place in hostile regions of difficult access and large areas. Introduction of new technologies such as Wireless Sensor Networks (WSNs) has allowed us to monitor such areas. In this paper, an intelligent system for fire prediction based on wireless sensor networks is presented. This system obtains the probability of fire and fire behavior in a particular area. This information allows firefighters to obtain escape paths and determine strategies to fight the fire. A firefighter can access this information with a portable device on every node of the network. The system has been evaluated by simulation analysis and its implementation is being done in a real environment.