Roman Lara-Cueva

Determining the Main CSMA Parameters for Adequate Performance of WSN for Real-Time Volcano Monitoring System Applications

This paper presents a study in order to identify the value range of the main parameters within carrier sense multiple access (CSMA) defined in IEEE 802.15.4 that guarantees a satisfactory wireless sensor network (WSN) performance for a volcano monitoring application. Moreover, this study performs the comparison among several test-beds in outdoor scenarios with the purpose of distinguishing the optimal number of nodes for each gateway according the main constraints given by an existing sensor network for real-time (RT) volcano monitoring system such as sampling time, packet loss, and delay. We used a mathematical model that works with Markovian techniques and involves some parameters of CSMA mechanism within the model, such as the minimum value of the backoff exponent (BEmin), the contention window length (W), and the number of slots (L). We obtained the approximate values of these parameters by the interpolation of the normalized throughput curves from the deployment, and thus, we could obtain a mathematical model with the specifications required for the RT volcano monitoring. After validating the model with test-bed outdoor deployments we found that BEmin, W and L are key factors for determining the performance of a WSN, these parameters guarantee the range in which the WSN works according to the constraints imposed for this particular volcano monitoring application.

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high accuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

Time and Frequency Feature Selection for Seismic Events from Cotopaxi Volcano

This paper presents a study to select the most relevant features for classification of seismic signals obtained from the Cotopaxi Volcano, in the time and frequency domain. Fourier and Wavelet transform were used in the analysis. A total of 79 different features were used for the study. Feature selection was performed by CART by using Gini, Standard Deviation, Twoing Rule, Gram-Schmidt, and Interaction Information as relevant indices. A comparative analysis of the features obtained indicates that the most relevant features for the identification of seismic events are: Maximum Peak Value in the 10-20 Hz range, High Frequency in WT A6 and the Percentage of Energy in the D2 and D5 WT levels.

A new structure for sequential detection and maximum entropy spectral estimator for characterization of volcanic seismic signals

This study proposes a new method to characterize volcanic seismic events based on classic spectral and maximum entropy estimators. Events of interest are detected in the time domains by a new structure of sequential robust detection obtained using autoregressive spectral analysis. Classical power spectral density analysis is then used to define spectral features for each type of seismic events of interest. A data set of seismic events from Cotopaxi volcano was used for the analysis. The proposed method allows near-real time detection of the locations in time where certain volcanic events take place, maximizing the detection probability and maintaining a constant false alarm rate.

Performance Analysis of Wireless Network Modes in Conformance with IEEE 802.11b and WDS

Usage of wireless networks is rising in popularity. In this setting different kinds of networks for indoor environments, such as Ad-Hoc, IEEE 802.11b (Wi-Fi) and Wireless Distribution System (WDS) has been developed. In this paper, an analysis of Quality of Service (QoS) has been made for each network in order to verify the values of bit rate, delay, jitter and packet loss. For performance comparison of these networks, injection of UDP traffic was accomplished by means of the Distributed Internet Traffic Generator (D-ITG). Distance and number of obstacles were varied in several deployed network scenarios. In each scenario, QoS parameters were measured and efficiency was estimated. According to results, WDS networks have better performance which has 30% more efficiency in long distance communication than Ad-Hoc and 802.11 networks.

Determining the number of nodes in a volcano monitoring system by using WSN

Volcano monitoring systems by using Wireless Sensor Networks (WSN) must ensure an adequate network performance, which — in fact — depends on the number of network nodes. This paper is focused on determining the number of nodes, which collectively are able to yield an adequate network performance. We implemented function optimization methods and QoS metric analysis to find the effective number of sensors. It is guaranteed that the network is competent and meets the requirements of volcano monitoring systems. The process was arranged in three phases: Network WSN on the ns-2 simulator was implemented with randomized distribution. The transmission of information was collected for 2 minutes of simulation, showing tolerable results of average delay in units of milliseconds. The second stage corresponds to the deduction of the algebraic objective function, and the last includes the application of nonlinear optimization methods on objective function. The results show that the optimal number of nodes is 10, it ensures packet loss less than 20%

Real-Time Seismic Event Detection Using Voice Activity Detection Techniques

Seismic event detection is a key element for volcano monitoring systems. Real-time event detection is required by early warning monitoring systems in order to minimize the possible impact of natural disasters of geophysical nature. In this paper, we propose to implement a real-time long period (LP) and a volcano-tectonic (VT) event detector based on voice activity detection algorithms. The main advantage of such detector is that it can also locate the endpoints of the seismic event. In order to determine the efficiency of the proposed detector, a database containing 436 seismic events (LP and VT) acquired from the Cotopaxi volcano in Ecuador was used for testing. Main performance parameters such as accuracy (A), precision, sensitivity or recall, specificity, and the balanced error rate (BER) were then calculated, finally the processing time required by the algorithm was also considered. The results obtained suggest comparable performance when compared to previously developed event detection algorithms for the same dataset but with much less computational complexity, achieving an A of 95.2% and a BER of 0.005. With further refinements the algorithm may provide a useful tool for real-time volcanic research.