An empirical study of power consumption of Web-based communications in mobile phones

Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.MUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Model Driven Evolution of an Agent-Based Home Energy Management System

Advanced smart home appliances and new models of energy tariffs imposed by energy providers pose new challenges in the automation of home energy management. Users need some assistant tool that helps them to make complex decisions with different goals, depending on the current situation. Multi-agent systems have proved to be a suitable technology to develop self-management systems, able to take the most adequate decision under different context-dependent situations, like the home energy management. The heterogeneity of home appliances and also the changes in the energy policies of providers introduce the necessity of explicitly modeling this variability. But, multi-agent systems lack of mechanisms to effectively deal with the different degrees of variability required by these kinds of systems. Software Product Line technologies, including variability models, has been successfully applied to different domains to explicitly model any kind of variability. We have defined a software product line development process that performs a model driven generation of agents embedded in heterogeneous smart objects with different degrees of self-management. However, once deployed, the home energy assistant system has to be able to evolve to self-adapt its decision making or devices to new requirements. So, in this paper we propose a model driven mechanism to automatically manage the evolution of multi-agent systems distributed among several devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models

Edge Computing proposes to use the nearby devices in the frontier/Edge of the access network for deploying application tasks of IoT-based systems. However, the functionality of such cyber–physical systems, which is usually distributed in several devices and computers, imposes specific requirements on the infrastructure to run properly. The evolution of an application to meet new user requirements and the high diversity of hardware and software technologies in the IoT/Edge/Cloud can complicate the deployment of continuously evolving applications. The aim of our approach is to apply Multi Layer Feature Models, which capture the variability of applications and the software and hardware infrastructure, to support the deployment in edge-based environments of cyber–physical applications. With this multi-layered approach is possible to support the evolution of application and infrastructure independently. Considering that IoT/Edge/Cloud infrastructures are usually shared by many applications, the deployment process has to assure that there will be enough resources for all of them, informing developers about the feasible alternatives. We provide four modules so that the developer can calculate what is the configuration of minimal set of devices supporting application requirements of the evolved application. In addition, the developer can find what is the application configuration that can be hosted in the current infrastructure. The successive solutions of continuous deployment generated by our approach pursue the reduction of the system energy footprint and/or execution latency.This work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds, Spain LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081. Funding for open access charge: Universidad de Málaga/CBUA

Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures

In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applicationsHADAS TIN2015-64841-R (co-funded by FEDER funds), TASOVA MCIU-AEI TIN2017-90644-REDT, MEDEA RTI2018-099213-B-I00 (co-funded by FEDER funds) LEIA UMA18-FEDERJA-157 (co-funded by FEDER funds) Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Energy-efficient Deployment of IoT Applications in Edge-based Infrastructures: A Software Product Line Approach

In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. However, current task offloading approaches are often quite simple. They neither consider the high diversity of hardware and software technologies present in edge network devices, nor take into account that some tasks may require some specific software and hardware infrastructure to be executed. This paper proposes a task offloading process that leans on Software Product Line technologies, which are a very good option to model the variability of software and hardware present in edge environments. Firstly, our approach automates the separation of application tasks, considering the data and operation needs and restrictions among them, and identifying the hardware and software resources required by each task. Secondly, our approach models and manages separately the infrastructure available for task offloading, as a set of nodes that provide certain hardware and software resources. This separation allows to reason about alternative offloading of tasks with different hardware and software resource requirements, in heterogeneous nodes and minimizing energy consumption. In addition, the offloading process considers alternative implementations of tasks to choose the one that best fits the hardware and software characteristics of available edge network infrastructure. The experimental results shows that our approach reduces the energy consumption in the user node by approximately 41%–62%, and the energy consumption of the devices involved in a task offloading solution by 34-48%Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Self-Adaptation of mHealth Devices: The Case of the Smart Cane Platform

Nowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery saving and these other concerns. This work illustrates how to approach the self-adaptation of smart assistive devices by means of a DSPL-based strategy that optimizes battery consumption taking into account other QoS parameters at the same time. We illustrate our proposal with a real case study: a Smart Cane that is integrated with a DSPL platform, Tanit. Experimentation shows that it is possible to make a trade-off between different quality concerns (energy consumption and relative error). The results of the experiments allow us to conclude that the Tanit approach elongates battery duration of the Smart Cane in one day (an increase of a 6% with a relative error of 1%), so we improve the user quality of experience and reduce the energy footprint with a reasonable relative error.This research was funded by the projects Magic P12-TIC1814 and TASOVA MCIU-AEI TIN2017-90644-REDT, by the projects co-financed by FEDER funds HADAS TIN2015-64841-R, MEDEA RTI2018-099213-B-I00 and LEIA UMA18-FEDERJA-157, by the post-doctoral plan of the University of Málaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health Plus at Mälardalen University, Sweden. -Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Sistema de asignación de tareas energéticamente eficiente en infraestructuras de despliegue variables

Cada vez existen más dispositivos de la Internet de las Cosas conectados a Internet que generan una gran cantidad de datos que pueden llegar a congestionar la red en su camino hacia la Nube. Para paliar esta congestión, tecnologías recientes, como el Edge Computing y el Fog Computing, proponen realizar el procesamiento de los datos en dispositivos más cercanos al origen de estos datos. Esto hace que las infraestructuras sobre las que se despliegan las aplicaciones sean cada vez más variables (diferentes tipo de dispositivos, capacidades de cómputo, características de red, etc). En este trabajo se presenta una solución para la asignación óptima de tareas a dispositivos del borde, con el objetivo de minimizar el consumo energético de la ejecución de las aplicaciones. Para ello, utilizamos modelos de variabilidad de Lineas de Producto Software para configurar tanto las aplicaciones como las infraestructuras de despliegue, presentando un modelo general para este último. La configuración de ambas se utiliza como entrada a un marco de trabajo de asignación óptima de tareas, obteniendo como resultado un sistema que proporciona la configuración más eficiente energéticamente en el momento en que el usuario lanza la aplicación, sin comprometer su experiencia como usuario, de forma transparente, escalable, y consiguiendo un importante ahorro energético, como se demuestra en nuestro caso de estudio.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Role of age and comorbidities in mortality of patients with infective endocarditis.

The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups: A total of 3120 patients with IE (1327  There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in th

Contemporary use of cefazolin for MSSA infective endocarditis: analysis of a national prospective cohort

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P <0.01) and previous renal failure (57.9%, P <0.01). Patients treated with cloxacillin presented higher rates of septic shock (25%, P = 0.033) and new-onset or worsening renal failure (47.3%, P = 0.024) with significantly higher rates of in-hospital mortality (38.5%, P = 0.017). One-year IE-related mortality and rate of relapses were similar between treatment groups. None of the treatments were identified as risk or protective factors. Conclusion: Our results suggest that cefazolin is a valuable option for the treatment of MSSA IE, without differences in 1-year mortality or relapses compared with cloxacillin, and might be considered equally effective