Project

SDAS-RP: Intelligent Embedded Systems

Goal: This researh program covers the following areas:

- Wireless sensor networks.
- Wearable sensors.
- Embedded systems for biomedical applications.
- Embedded systems for precision agriculture.
- Battery and memory usage optimization.
- Speeding-up and discretization of machine learning algorithms.

Find more info at: https://sdas-group.com/intelligent-embedded-systems/

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Project log

Diego Peluffo
added a research item
IoT devices play a fundamental role in the machine learning (ML) application pipeline, as they collect rich data for model training using sensors. However, this process can be affected by uncontrollable variables that introduce errors into the data, resulting in a higher computational cost to eliminate them. Thus, selecting the most suitable algorithm for this pre-processing step on-device can reduce ML model complexity and unnecessary bandwidth usage for cloud processing. Therefore, this work presents a new sensor taxonomy with which to deploy data pre-processing on an IoT device by using a specific filter for each data type that the system handles. We define statistical and functional performance metrics to perform filter selection. Experimental results show that the Butterworth filter is a suitable solution for invariant sampling rates, while the Savi–Golay and medium filters are appropriate choices for variable sampling rates.
Diego Peluffo
added 5 research items
This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.
nside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the knee-joint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient’s knee-joint kinematics during a human gait.
Nowadays, the implementation of embedded systems with sensors for massive data collection has become widely used for their flexibility and improvement in decision making. However, this process can be affected by errors in reading, attrition of systems, among others. For this, a selection approach of supervised algorithms with a prototypes selection criterion is presented, which allows an adequate embedded system performance. To do that a quality measure was established which compromises between the data reduction of the training set, algorithm processing time and the classification performance. As a result, it was determined that the algorithm for the data selection is Condensed Nearest Neighbors (CNN) and the classification algorithm is k-Nearest Neighbour (k-NN).
Victor Asanza
added 3 research items
The implementation of a turkey farm brings with it severe environmental problems due to the deficient study of the physical space where the animals are placed. To counteract this situation and improve the quality of life in the hatchery, it is necessary to monitor and control the following variables: Temperature, Humidity, Ammonia Emission and Lux. The solution is based on a cyber-physical system which is composed of a network of sensors, controller and actuator. The sensors will provide information from the physical environment, the controller evaluates these parameters to execute an action to the actuator. Proportional, Integral and Derivative (PID) control defines the setpoint for temperature while Pulse- Width Modulation (PWM) adjusts the light intensity in a spotlight. The End Device executes these actions and its parameters will be sent to ThingSpeak which monitors system behavior the Internet of Things.
Abstract—This project analyses the optimal parameters for the shrimp farming, trying to help the aquaculture of Ecuador, using a cyberphysical system, which includes temperature, salinity, dissolved oxygen, and pH sensors to monitor the water conditions and an embedded system to control it using an XBee and ATMega328p microcontrollers to remotely activate and deactivate aerators to maintain the quality of each pool in neat conditions.
This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
Victor Asanza
added 5 research items
At this moment in the field of FPGA, only RAM tests have been carried out to evaluate its performance but these works have not focused on tracking memory usage in real time, this paper proposes a design for monitoring the memory of an embedded system, in the logical part, making use of the communication between the FPGA and the HPS. In addition, the HPS has implemented a web service that allows to visualize a graph of the monitoring in real time. The proposed design can be an introduction to the development of applications that can be specifically monitored for a component of the embedded system in FPGA, because FPGA is currently being used for different purposes such as machine learning, real-time image processing, mining of Bitcoins, among others. These applications are quite robust, which implies a high demand for processing for the embedded system.
In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
This paper focuses on analyzing health problems derived from a sedentary lifestyle. Studies seeking to improve physical activity have shown that a good incentive to increase physical activity requires social feedback, allowing the subject to keep motivated and competitive, along with a feedback of number of steps at the end of the day. This work describes the training and implementation of a neural network as an artificial intelligence model to predict the behavior of an individual, taking advantage of the flexibility provided by Field Programmable Gate Arrays (FPGAs). We propose the design of an edge computing system, analyzing the efficiency on power, area and computational performance. The results are presented through a display, making a comparison of the predicted and expected steps.
Diego Peluffo
added 4 research items
Esta investigación presenta el diseño de un prototipo para la detección de arritmias cardíacas que incorpora un sistema embebido de bajo costo computacional en un entorno de recursos computacionales limitados capaz de analizar características de los complejos QRS. Para ello, se desarrolla una estrategia de clasificación de latidos cardiacos, normales y patológicos, en registros electrocardiográficos de larga duración (Holter), los cuales son ondas representativas del latido y su análisis permite identificar arritmias de tipo ventricular. Para el desarrollo de este prototipo inicial, se comprueba que el uso del algoritmo de k vecinos más cercanos (k-NN) junto con una etapa de selección de variables del conjunto de entrenamiento es una buena alternativa y representa una contribución importante de este trabajo a nivel experimental. Los experimentos se realizaron sobre la base de datos de arritmias cardiacas del Massachusetts Institute of Technology (MIT). Los resultados son satisfactorios y promisorios. Palabras-clave: Arritmia cardíaca, Arduino Mega, base de datos de MIT, señales ECG de tipo Holter Design of a low computational cost prototype for cardiac arrhythmia detection Abstract: This research presents the design of a prototype for the detection of cardiac arrhythmias that incorporates an embedded low-cost computational system in an environment of limited computational resources capable of analyzing
This work presents a new approach to the Internet of Things (IoT) between sensor nodes and data analysis with visualization platform with the purpose to acquire urban pollution data. The main objective is to determine the degree of contamination in Ibarra city in real time. To do this, for one hand, thirteen IoT devices have been implemented. For another hand, a Prototype Selection and Data Balance algorithms comparison in relation to the classifier k-Nearest Neighbourhood is made. With this, the system has an adequate training set to achieve the highest classification performance. As a final result, the system presents a visualization platform that estimates the pollution condition with more than 90% accuracy.
One way to identify musculoskeletal disorders in the lower limb is through the functional examination where the ranges of normality of the joints are evaluated. Currently, this test can be performed with technological support, with optical sensors and inertial measurement sensors (IMU) being the most used. Kinect has been widely used for the functional evaluation of the human body, however, there are some limits to the movements made in the depth plane and when there is occlusion of the limbs. Inertial measurement sensors (IMU) allow orientation and acceleration measurements to be obtained with a high sampling rate, with some restrictions associated with drift. This article proposes a methodology that combines the acceleration measures of the IMU and kinect sensors in two planes of movement (Frontal and sagittal). These measurements are filtered in the preprocessing stage according to a Kalman filter and are obtained from a mathematical equation that allows them to be merged. The fusion system data obtains acceptable RMS error values of 5.5\(^{\circ }\) and an average consistency of 92.5% for the sagittal plane with respect to the goniometer technique. The data is shown through an interface that allows the visualization of knee joint kinematic data, as well as tools for the analysis of signals by the health professional.
Diego Peluffo
added a research item
Car drivers under the influence of alcohol is one of the most common causes of road traffic accidents. To tackle this issue, an emerging, suitable alternative is the use of intelligent systems -traditionally based on either sensor networks or artificial vision- that are aimed to prevent starting the car when drunk status on the car driver is detected. In such vein, this paper introduces a system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data. To do so, some drunk-status criteria are considered, namely: the concentration of alcohol in the car environment, the facial temperature of the driver and the pupil width. Specifically, for data acquisition purposes, the proposed system incorporates a gas sensor, temperature sensor and a digital camera. Acquired data are analyzed into a two-stages machine learning system consisting of feature selection and supervised classification algorithms. Both acquisition and analysis stages are to be performed into a embedded system, and therefore all procedures and algorithms are designed to work at low-computational resources. As a remarkable outcome, due mainly to the incorporation of feature selection and relevance analysis stages, proposed approach reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system.
Diego Peluffo
added 2 research items
This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption.
This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation. A data analysis process was carried out, aimed at designing an in-situ intelligent system able to make proper decisions regarding the cultivation process. This process involves stages for balancing data, prototype selection, and supervised classification. The proposed system produces a significant reduction of data in the training set obtained by the WSN while reaching a high classification performance in real conditions—amounting to 90% and 97.5%, respectively. As a remarkable outcome, it is also provided an approach to ensure correct planning and selection of routes for the autonomous vehicle through the global positioning system.
Paul Rosero
added 2 research items
Internet de las cosas (o también conocido como IoT) es una de las tecnologías más nombradas en la actualidad debido a la capacidad que prevé para conectar todo tipo de dispositivos al Internet, si a las potencialidades de IoT le adicionamos otra tecnología de alto impacto como lo es la Visión Artificial tenemos un amplio campo de aplicaciones innovadoras, donde el procesamiento de imágenes y video en tiempo real permiten la visualización de grandes cantidades de datos en internet. Las principales aplicaciones que se desarrollan con IoT y Visión Artificial pueden ser implementadas en educación, medicina, edificios inteligentes, sistemas de vigilancia de personas y vehículos, entre otros. Este tipo de aplicaciones mejoran la calidad de vida de los usuarios, sin embargo, para su desarrollo se requiere una infraestructura que permita la convergencia de diferentes protocolos y dispositivos, pero de manera especial que puedan manejar las diferentes fases de la adquisición de imágenes. En este trabajo se ha realizado una revisión de los inicios, conceptos, tecnologías y aplicaciones ligados a la Visión Artificial con el Internet de las Cosas para poder comprender de forma precisa el impacto de sus aplicaciones en la vida cotidiana.
Diego Peluffo
added 4 research items
The use of electronic systems and devices has become widely spread and is reaching several fields as well as indispensable for many daily activities. Such systems and devices (here termed embedded systems) are aiming at improving human beings’ quality of life. To do so, they typically acquire users’ data to adjust themselves to different needs and environments in an adequate fashion. Consequently, they are connected to data networks to share this information and find elements that allow them to make the appropriate decisions. Then, for practical usage, their computational capabilities should be optimized to avoid issues such as: resources saturation (mainly memory and battery). In this line, machine learning offers a wide range of techniques and tools to incorporate “intelligence” into embedded systems, enabling them to make decisions by themselves. This paper reviews different data storage techniques along with machine learning algorithms for embedded systems. Its main focus is on techniques and applications (with special interest in Internet of Things) reported in literature about data analysis criteria to make decisions.
Paul Rosero
added a research item
The sports ergonomics study allows a bio-mechanical analysis in order to evaluate the impact produced by different muscle conditioning exercises such as the squat. This exercise, if carried out in an erroneous way, it can cause lumbar injuries. The present electronic system acquire the data of the Smith bar and the back by means of accelerometer sensors. This is done in order to implement an intelligent algorithm that allows to recognize if the athlete performs the exercise properly. For this, a stage of prototypes selection and a comparison of classification algorithms (CA) is carried out. Finally, a quantitative measure of equilibrium between both criteria is established for its proper selection. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves a 96% performance and a 50% training matrix reduction.
Diego Peluffo
added 3 research items
The research area of sitting-pose analysis allows for preventing a range of physical health problems mainly physical. Despite that different systems have been proposed for sitting-pose detection, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an alternative approach based on a sensor network to acquire the position-related variables and machine learning techniques, namely dimensionality reduction (DR) and classification. Since the information acquired by sensors is high-dimensional and therefore it might not be saved into embedded system memory, a DR stage based on principal component analysis (PCA) is performed. Subsequently, the automatic posed detection is carried out by the k-nearest neighbors (KNN) classifier. As a result, regarding using the whole data set, the computational cost is decreased by 33 % as well as the data reading is reduced by 10 ms. Then, sitting-pose detection task takes 26 ms, and reaches 75% of accuracy in a 4-trial experiment.
Es un texto para comprender el manejo de diodos y transistores para aplicarlos en rectificación de voltaje alterno a continuo y las formas de amplificar la corriente según las diferentes polarizaciones de transistores y sus métodos de conmutación
RESUMEN Este trabajo presenta el diseño de un prototipo de bajo costo para aplicaciones de biofeedback basado en la frecuencia cardiaca estimada por fotopletismografía. El prototipo propuesto involucra principalmente dos bloques constructivos: El primero de ellos se ocupa de la adquisición y filtrado análogo de la señal. Mientras tanto, el segundo bloque consiste en la estimación de la frecuencia cardiaca y la retroalimentación visual, implementadas en una plataforma Arduino. Los resultados experimentales y pruebas muestran su utilidad, manteniendo el objetivo de involucrar bajos costos y portabilidad. ABSTRACT This work presents the design of a low-cost biofeedback prototype based on the heart rate estimated from photoplethysmography. Proposed prototype involves mainly two building blocks: The first one is concerned on the signal acquisition and analog filtering. Meanwhile, the second one is the estimation of heart rata and visual feedback implemented in an Arduino platform. Experimental results and tests show its usability keeping the goal of involving design low costs and portability.
Paul Rosero
added 3 research items
En la presente investigación se desarrolla un sistema electrónico de seguridad para bebés en etapa de gateo, con el fin de alertar de un posible accidente a las personas que están a cargo de su cuidado en áreas consideradas riesgosas. El textil inteligente se encuentra integrado en una prenda de vestir tipo arnés que se conecta a unas rodilleras de gateo que contienen sensores Magnetic Reed, éstos se encargan de detectar las cintas magnéticas colocadas previamente en los sitios considerados peligrosos. Éstas áreas se eligen mediante una cuantificación de la distancia del bebé en relación a su velocidad de gateo, si el infante se encuentran en una posible zona de accidente el sistema envía mensajes de alerta mediante una plataforma IoT. Como resultado del sistema se tiene un tiempo medio de reacción de 7,6 segundos con un consumo promedio de corriente de 90 mA cada 12 segundos aproximadamente.
This work is a Scientific Track paper corresponding to the area of Intelligent Systems. This paper presents a facial recognition approach based on the Eigenfaces method as well as Principal Component Analysis (PCA) as algorithm of processing and cleaning images, respectively. The classification was performed by using the Euclidean distance between the facial characters stored in a database and new images captured in an interface with similarly coded developed in MatLab. As main results, we obtained: (i) 68.9% of classification accuracy when using different components of stored faces, (ii) 91.43% of classification performance when storing 3 components for each face and evaluating more users for training model in seven controlled experiments.
The present work presents an analysis of the neighborhood criterion for the prototype selection (PS) in supervised machine learning classification algorithms. To do this, we use the condensed neighbor algorithm CNN to eliminate redundant data with the normalization of the distance to the centroid of each data subset criterion. This is done, in order to obtain the training matrix of the most optimal model. A selection of neighborhood criterion has been created from the quantification of the balance between the performance of the classification and the reduction of data set (CER). As proof of the test, we performed: (i) CER and (ii) real-time tests with the implementation of the algorithm within the WSN. The result is a data reduction of up to 88 % and a performance of the kNN classifier of 75%. It is concluded that the criterion of neighborhood with normalized distance must be less than or equal to 0.2 and the implementation of kNN with k = 1 obtains the best CER.
Paul Rosero
added a research item
El gateo de los bebés es la forma de descubrir y aprender sus funciones motoras, cognitivas, sociales, emocionales, entre otros. Debido a esto, los infantes se exponen a diferentes peligros como: caídas, quemaduras, lesiones corporales entre las más comunes, donde el hogar es el lugar de mayores incidentes. Debido a lo antes expuesto, la presente investigación se enfoca en el desarrollo de un sistema embebido dentro de un textil inteligente que permita advertir y prevenir accidentes. El sistema se encuentra en una prenda de vestir en forma de arnés que cuentan con una conexión con rodilleras de gateo con sensores magnéticos. Estos elementos son los encargados de detectar cintas de seguridad magnéticas colocadas previamente en lugares de mayor peligro en el hogar. Como resultado, el sistema emite una alerta con un tiempo de reacción de 7,6 segundos, después de ser activado el sistema.
Paul Rosero
added a research item
Este libro se ha realizado con los objetivos de brindar los conocimientos básicos de programación y funcionamiento de un sistema embebido en plataformas libres. Arduino se ha convertido en una herramienta muy importante para el prototipado de innovaciones tecnológicas, su bajo costo y amplias referencias en el internet. Esto permite disminuir la curva de aprendizaje de los estudiantes en áreas de ingeniería. Se encuentra estructurado de la forma que cada capítulo sea las bases de aprendizaje para los siguientes contenidos.
Paul Rosero
added a research item
The analysis of human sit down position is a research area allows for preventing health physical problems in the back. Many works have proposed systems that detect the sitting position, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an alternative approach based on an embedded system to acquire the position-related variables and machine learning techniques, namely dimensionality reduction (DR) and classification. Since the information acquired by sensors is high-dimensional and therefore it might not be saved into embedded system memory, for this reason the system has a DR stage based on principal component analysis (PCA) is performed. Subsequently, the posed detection is carried out by the k-nearest neighbors (KNN) classifier between the matrix stored in the system and new data acquired by pressure and distance sensors. Thus, regarding using the whole data set, the computational cost is decreased by 33 % as well as the data reading is reduced by 10 ms. Then, sitting-pose detection task takes 26 ms, and reaches 75% of accuracy in a 4-trial experiment.
Paul Rosero
added a research item
Los sistemas electrónicos con el avance tecnológico tienden a conectarse a redes de datos para brindar mejores servicios a las personas, Internet de las Cosas es la forma de comunicar remotamente dispositivos que anteriormente no lo hacían, la comunicación M2M (machine to machine) deber ser lo más confiable posible para garantizar la transferencia de datos, TCP y UDP son protocolos de capa transporte del modelo TCP/IP para implementar una red de sensores con miras a aplicaciones a Internet de las Cosas, plataformas libres como Intel Galileo Gen 2 permiten realizar prototipos de una forma rápida y efectiva para presentar soluciones con un alto y óptimo procesamiento de información que den paso a la conectividad a redes. TCP permite el envío confiable de altas tasas de datos a la red lo que influye en el tiempo de respuesta del procesador y de la red, mientras que UDP es liviano de carga computacional, pero no permite conocer si el dato ha llegado a su destino.
Paul Rosero
added 2 research items
Resumen: Los géneros textiles han sufrido grandes cambios a una velocidad impresionante, dando como re-sultado a la aparición de los textiles inteligentes; la primera generación de los textiles inteligentes solamente pueden detectar las condiciones medioambientales o estímulos del mismo, la segunda generación son los textiles que tienen la capacidad de detectar y actuar frente a una determinada situación y los textiles ultra inteligentes son la tercera generación, mismos que pueden detectar, reaccionar y adaptarse a las condiciones y estímulos del medio. Un textil ultra inteligente esencialmente consiste en una unidad, la cual trabaja como cerebro, con capa-cidad cognitiva, que razona y reacciona, siendo la unión de diferentesáreasdiferentes´diferentesáreas de conocimiento para su desarrollo y conocidos como Smart clothes. En el futuro los textiles inteligentes se presentan como la próxima generación de fibras, tejidos y productos que se producirán gracias a sus enormes posibilidades y funcionalidades. Palabras claves: Evolución textil, textiles inteligentes, smart clothes 9.1. Introducción Indica [1] que el 50 % de los elementos que nos rodean con los cuales tenemos mayor contacto en el día a día son textiles, o por lo menos tienen involucrado un textil en su composición. Aviones, automóviles, oficinas, muebles, camas, baños, decoraciones, entre otros, y por supuesto el multivariado y multipropósito tema del vestuario; son ejemplos claros que nos muestran que los textiles tienen un gran contacto con el ser humano en su vida cotidiana Los textiles están en constante desarrollo con el fin de obtener de ellos propiedades especiales que les hacen aptos para su aplicación en diferentes campos de la actividad humana e industrial, desde la cirugía, hasta los deportes de competición para mejorar resultados; la vestimenta de los astronautas, facilitarnos mayor confort, la construcción y el acondicionamiento de locales, vencer el frío, olvidarnos del calor, pasando por otros muchos campos en los que se aplican estas innovaciones [2]. Tecnologías Aplicadas a la Ingeniería: FICA-UTN
Resumen— La evolución de Internet ha tenido un alto impacto para la población mundial, aunque en sus inicios solo se consideró como una red de computadores ha pasado a convertirse en la base del desarrollo de la mayoría de aspectos de la vida diaria de todas las personas. La relación entre dispositivos conectados y el número de usuarios que se ha establecido en los últimos años indica que existen más sistemas electrónicos conectados a Internet que habitantes a nivel mundial. El Internet de las Cosas (IoT) se origina por la necesidad de estar conectados entre todos, esto incluye a personas y cosas, de tal manera que cada individuo pueda gestionar de forma remota a cualquier objeto que tenga una conexión a Internet. Esto ha requerido emplear diversas tecnologías, la que mayor éxito ha tenido son las redes de sensores inalámbricos, debido a que permiten recoger diferentes tipos de datos para luego poder ser monitorizados y gestionados. Varias organizaciones a nivel mundial se han encargado de trabajar en la arquitectura y los protocolos que se deben tomar en cuenta para desarrollar aplicaciones de IoT con el fin de que exista compatibilidad entre dispositivos. Una revisión del estado del arte de IoT y WSN permite tener una visión más amplia del alcance, arquitectura, aplicaciones y limitaciones de estas tecnologías, especialmente enfocados al manejo de seguridad de la información y Big Data.
Paul Rosero
added a project goal
This researh program covers the following areas:
- Wireless sensor networks.
- Wearable sensors.
- Embedded systems for biomedical applications.
- Embedded systems for precision agriculture.
- Battery and memory usage optimization.
- Speeding-up and discretization of machine learning algorithms.