David Sánchez-Rodríguez

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results.

Methodology for automatic bioacoustic classification of anurans based on feature fusion

Data fusion of temporal and frequency domain acoustic information.Anurans identification approach based on the parameterization of its sound.Experimental methodology for anuran species recognition.To develop a tool for generating knowledge on Biodiversity Conservation. The automatic recognition of anurans by their calls provides indicators of ecosystem health and habitat quality. This paper presents a new methodology for the acoustic classification of anurans using a fusion of frequency domain features, Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), with time domain features like entropy and syllable duration through intelligent systems. This methodology has been validated in three databases with a significant number of different species proving the strength of this approach. First, the audio recordings are automatically segmented into syllables which represent different anuran calls. For each syllable, both types of features are computed and evaluated separately as in previous works. In the experiments, a novel data fusion method has been used showing an increase of the classification accuracy which achieves an average of 98.80% ? 2.43 in 41 anuran species from AmphibiaWeb database, 96.90% ? 3.57 in 58 frogs from Cuba and 95.48% ? 4.97 in 100 anurans from southern Brazil and Uruguay; reaching a classification rate of 95.38% ? 5.05 for the aggregate dataset of 199 species.

A low consumption real time environmental monitoring system for smart cities based on ZigBee wireless sensor network

Nowadays, there is an increasing interest in wireless sensor networks (WSN) for environmental monitoring systems because it can be used to improve the quality of life and living conditions are becoming a major concern to people. This paper describes the design and development of a real time monitoring system based on ZigBee WSN characterized by a lower energy consumption, low cost, reduced dimensions and fast adaptation to the network tree topology. The developed system encompasses an optimized sensing process about environmental parameters, low rate transmission from sensor nodes to the gateway, packet parsing and data storing in a remote database and real time visualization through a web server. A monitoring system integrating the outlined system has been deployed and tested for monitoring the level of dust particles in the air, acoustic levels in different places of a city, ambient temperature and relative humidity. A calibration process of a low cost audio sensor was performed to measure the acoustic level from different noise sources, hence, it is not necessary to use an expensive sound level meter at each node. Furthermore, experimental results show autonomy nodes can be about three months.

Using bioacoustic signals and Support Vector Machine for automatic classification of insects

This work presents a new approach for automatic recognition of insects through intelligent systems. Insect species employ a set of sound signals for communication purposes which are specie-specific. Based on this fact, an acoustic signal recognition method has been designed to allow an efficient taxonomic classification of this animal group. In this paper, the sound signals have been characterized by Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs) to compare their efficacy. Then, a Support Vector Machine algorithm has been used for classification achieving an average success rate of 99.08% over 88 insect species.

Analysis of the effects of the hidden node problem in IEEE 802.15.7 uplink performance

IEEE 802.15.7 is a physical and MAC layer standard for visible light communication (VLC). A slotted CSMA/CA MAC procedure is defined in the standard to coordinate optical channel access for multiple wireless devices. This CSMA/CA process does not provide any hidden node avoidance mechanisms, thus potentially leading to severe performance degradation in the presence of hidden nodes due to collisions. In this paper, we analyze the effect of hidden nodes on a VLC network in the star topology. We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and packet loss rate. The impact of the hidden node problem in network performance is evaluated by comparing simulation results.

Implementing an IEEE802.15.7 Physical Layer Simulation Model with OMNET

Visible Light Communications (VLC) uses visible light spectrum as transmission medium for communications. VLC has gained recent interest as a favorable complement to radio frequency (RF) wireless communications systems due to the ubiquity and wide variety of applications. In 2011 the Institute of Electrical and Electronic Engineers published the standard IEEE 802.15.7 [1]. Nowadays, simulation tools are widely used to study, understand and achieve better network performance. This paper describes the design and implementation of a physical layer model based in IEEE802.15.7 standard using OMNET++ simulation tool [2]. This software is a popular tool for building networks’ and modeling their behavior. The main goal of this paper is to introduce the developing and implementing of a software module to simulate the Physical Layer (PHY) based on IEEE802.15.7. The developed module, called simVLC will let researchers and students to study and simulate different scenarios in this standard.

Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication

Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services. Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency. Nevertheless, those approaches usually deliver an accuracy of up to two metres, since they are hindered by multipath propagation. On the other hand, in the last few years, the increasing use of light-emitting diodes in illumination systems has provided the emergence of Visible Light Communication technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. This brings a brand new approach to high accuracy indoor positioning because this kind of network is not affected by electromagnetic interferences and the received optical power is more stable than radio signals. Our research focus on to propose a fingerprinting indoor positioning estimation system based on neural networks to predict the device position in a 3D environment. Neural networks are an effective classification and predictive method. The localization system is built using a dataset of received signal strength coming from a grid of different points. From the these values, the position in Cartesian coordinates (x,y,z) is estimated. The use of three neural networks is proposed in this work, where each network is responsible for estimating the position by each axis. Experimental results indicate that the proposed system leads to substantial improvements to accuracy over the widely-used traditional fingerprinting methods, yielding an accuracy above 99% and an average error distance of 0.4 mm.

Automatic classification of pinnipeds based on their vocalizations and fusion of cepstral features

Acoustic vocalizations are common in marine mammals which can be used for classification purposes. Pinnipeds are a group of carnivore mammals composed by seals, sea lions, and walruses. But although, there is a great interest in research literature about acoustic monitoring of marine mammals, the identification of pinnipeds trough experts systems has been poorly studied. This paper brings a novel method for the automatic taxonomic classification of pinnipeds using a fusion of Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), representing the acoustic signal in both low and high frequencies. In our experiment, we have used kNN for classification, achieving an average identification of 96.48% ± 9.17 over 18 pinniped species.