Person:
Ayala Rodrigo, José Luis

First Name

José Luis

Last Name

Ayala Rodrigo

URI

https://hdl.handle.net/20.500.14352/78677

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Informática

Department

Arquitectura de Computadores y Automática

Area

Arquitectura y Tecnología de Computadores

Identifiers

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Search Results

Now showing 1 - 10 of 22

Project number: 290
Arquitecturas dinámica de redes inalámbricas en banda libre para la ejemplificación de conceptos de transmisión en aplicaciones de “Internet Of Things”
(2016) Ayala Rodrigo, José Luis; Pagán Ortiz, Josué; Zapater Sancho, Marina; Del Barrio García, Alberto; Hermida Correa, Román
Este proyecto de innovación educativa propone una metodología práctica y un equipamiento novedoso para la docencia de la asignatura de Redes y Servicios de Telecomunicación II impartida en tercer curso del Grado en Ingeniería Electrónica de Comunicaciones. Mediante la incorporación de elementos prácticos a la docencia como los recogidos en este proyecto, se pretende ahondar en los conceptos de uso espectral, acceso a un canal compartido, enrutamiento, topología de red, relación consumo vs. potencia de transmisión, etc. desde una perspectiva práctica que facilite el aprendizaje y despierte la curiosidad del alumnado. Para ello, se propondrá un despliegue de nodos inalámbricos, y un entorno de programación de éstos, que permita la evaluación de los contenidos antes descritos.
A Link Quality Estimator for Power-Efficient Communication over On-Body Channels
(Proceedings - 2014 International Conference on Embedded and Ubiquitous Computing, EUC 2014, 2014) Recas Piorno, Joaquín; Ayala Rodrigo, José Luis; Vallejo, Mónica
The human body has an important effect on the performance of on-body wireless communication systems. Given the dynamic and complex nature of the on-body channels, link quality estimation models are crucial in the design of mobility management protocols and power control protocols. In order to achieve a good estimation of link quality in WBSNs, we combine multiple body-related factors into a model that includes: the transmission power, the body position, the body shape and composition characteristics and the received signal strength indicator (RSSI) as an indicator of link quality. In this paper, we propose the Anfis Link Quality Estimator (A-LQE) that has been trained with RSSI values measured at different transmission power levels in a sample of 37 human subjects. Once the accuracy and reliability of our proposed model have been analysed, we apply the model to adapt the transmission power to the link characteristics for energy optimization. The obtained average energy savings reach the 26% in comparison with the maximum transmission power mode.
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments
(Sensors, 2012) Zapater, Marina; Sanchez, Cesar; Ayala Rodrigo, José Luis; Moya, Jose M.; Risco Martín, José Luis
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.
An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices
(2017) Pagán Ortiz, Josué; Fallahzadeh, Ramin ; Ghasemzadeh, Hassan ; Moya, Jose M.; Risco Martín, José Luis; Ayala Rodrigo, José Luis
Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.
Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities.
(Energy Procedia, 6th International conference on sustainability in energy and buildings, 2014) Arroba, Patricia; Risco Martín, José Luis; Zapater Sancho, Marina; Moya, José Manuel; Ayala Rodrigo, José Luis; Olcoz Herrero, Katzalin
As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.
Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data
(Sensors, 2015) Pagán, Josué; De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco Martín, José Luis; Vivancos Mora, J.; Moya, José M.; Ayala Rodrigo, José Luis
Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.
Power-awareness and smart-resource management in embedded computing systems
(2015) Santambrogio, Marco Domenico; Ayala Rodrigo, José Luis; Campanoni, Simone; Cattaneo, Riccardo; Durelli, Gianluca Carlo; Ferroni, Matteo; Nacci, Alessandro; Pagán Ortiz, Josué; Zapater, Marina; Vallejo, Mónica
Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960's. Therefore this is the time for a fresh approach to the way systems are designed and used.
Runtime data center temperature prediction using Grammatical Evolution techniques
(Applied soft computing, 2016) Zapater, Marina; Risco Martín, José Luis; Arroba, Patricia; Ayala Rodrigo, José Luis; Moya, José M.; Hermida Correa, Román
Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications
(Sensors, 2013) Vallejo, Mónica; Recas, Joaquín; García del Valle, Pablo; Ayala Rodrigo, José Luis
The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead.
Método para determinar el nivel de activación del sistema trigémino-vascular
(2016) Gago Veiga, Ana Beatriz; Sobrado Sanz, Mónica; Vivancos Mora, José Aurelio; Pagán Ortiz, Josué; De Orbe Izquierdo, María Irene; Ayala Rodrigo, José Luis; Fundación para la Investigación Biomédica del Hospital Universitario La Princesa
The present invention describes a method for determining in real time the degree of activation of the trigeminovascular system. In particular, the invention can be applied in the field of medical devices capable of determining the activation index of the trigeminovascular system, mainly on the basis of the use of biomedical signals of hemodynamic character. The method establishes objective criteria for determining the degree of activation and is described as the result of the application of modelling and data fusion techniques. The method is also based on another type of signals, such as ambient signals, in order to improve statistically in real time the degree of activation determined.