An XRI naming system for dynamic and federated clouds: a performance analysis

Abstract Cloud platforms are dynamic, self-optimizing, continuously changing environments where resources can be composed with other ones in order to provide many types of services to their users, e.g., companies, governments, organizations, and desktop/mobile clients. In order to enable cloud platforms to manage and control their assets, they need to name, identify, and resolve their virtual resources in different operating contexts. In such a scenario, naming, resource location, and information retrieval raise several issues regarding name space management. This paper aims to propose a standard practice for the implementation of a cloud naming system based on the eXtensible Resource Identifier (XRI) technology. More specifically, by means of the development of a Cloud Name Space Management (CNSM) front-end interacting with the OpenXRI architecture, we investigate its performance simulating typical cloud name space management tasks

A Cloud-based System to Protect Against Industrial Multi-risk Events☆

Abstract Industrial areas frequently present a high concentration of production operations which are source of anthropic risks. For this reason Smart Public Safety is receiving an increasing attention from industry, research and authorities. Moreover, due the consequences of global warming, these areas could be subject to risk events with increased probability with respect to the past. Information technologies enable an innovative approach towards safety management, which relies on the evolution of tools for environmental monitoring and citizens' interaction. This work presents the preliminary results of the Italian research project SIGMA - sensor Integrated System in cloud environment for the Advanced Multi-risk Management. The proposed system includes a continuous monitoring of the different information sources, thus reducing human control as much as possible. At the same time, the communication system manages multiple data flows in a flexible way, adapting itself to different working scenarios, enabling smarter applications. SIGMA intends to acquire, integrate and compute heterogeneous data, coming from various sensor networks in order to provide useful insights for the monitoring, forecasting and management of risk situations through services provided to citizens and businesses, both public and private. Based on the integration of different interoperating components, the system is able to provide a complete emergency management framework through simulations/optimizations and heterogeneous data manipulation tools. The prototype solution is detailed by a use case application in an industrial area located in the region of Sicily, Italy. In particular, web based modular applications connected through SIGMA allow the monitoring of the industrial environment through data gathering from different sensor networks, such as outdoor sensors mounted in the surroundings of large industrial areas, and support of the design of the logistics network aimed at covering the industrial risks

A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

Categorías urbanísticas y climáticas integradas para estudios microclimáticos del confort térmico urbano

El objeto de este estudio es adoptar y proponer una clasificación en la microescala más detallada de las condiciones urbanas intra-ciudad, en vistas a una optimización del confort higrotérmico, teniendo en cuenta la influencia que pudiera ejercer el tipo de uso del suelo, la forestación y las estructuras urbanas construidas. Se aplican para ello las Áreas Muestras de Estudio para el Área Metropolitana de San Juan, en el marco del sistema de clasificación internacional de Zonas Climáticas Locales. Se actualizaron las áreas seleccionadas en trabajos anteriores usando las Bandas Urbanas Características. Esta nueva selección de puntos de muestreo y clasificación climática urbana, será en posterior trabajo objeto de mediciones y estudios meteorológicos y de calidad del aire especifícos, que permitan evaluar la influencia de la forestación de los Canales Viales Urbanos (CVU) en la mitigación de algunos problemas ambientales producidos por la Isla de Calor. El trabajo logra integrar conceptos propios de la espacialización urbanística de la ciudad de San Juan con criterios de clasificación climática normalizados. Se espera que esta clasificación sea suficientemente general como para permitir comparaciones con áreas similares de distintas ciudades del mundoFil: Roca, Gabriela Sofía. Universidad Nacional de San Juan. Facultad de Arquitectura, Urbanismo y Diseño; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Puliafito, Salvador Enrique. Universidad Nacional de San Juan. Facultad de Arquitectura, Urbanismo y Diseño; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kurban, Alejandra Silvia. Universidad Nacional de San Juan. Facultad de Arquitectura, Urbanismo y Diseño; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cunsulo, Francisco Antonio. Universidad Nacional de San Juan. Facultad de Arquitectura, Urbanismo y Diseño; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

GSM-RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10  MHz bandwidth centered at the frequency 945  MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network

Editorial: Special issue on Internet of Things: convergence of sensing, networking, and web technologies

International audienc

Combining Cloud and sensors in a smart city environment

International audienceIn the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones, sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume (data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or "things" in the Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a "Cloud of Things". In the Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences in the "appliances" being sensed, smart sensors are very heterogeneous in terms of communication technologies, sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, thus creating new opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT. Smart cities are assumed as the reference scenario

Optimal Selection Techniques for Cloud Service Providers

Nowadays Cloud computing permeates almost every domain in Information and Communications Technology (ICT) and, increasingly, most of the action is shifting from large, dominant players toward independent, heterogeneous, private/hybrid deployments, in line with an ever wider range of business models and stakeholders. The rapid growth in the numbers and diversity of small and medium Cloud providers is bringing new challenges in the as-a-Services space. Indeed, significant hurdles for smaller Cloud service providers in being competitive with the incumbent market leaders induce some innovative players to "federate" deployments in order to pool a larger, virtually limitless, set of resources across the federation, and stand to gain in terms of economies of scale and resource usage efficiency. Several are the challenges that need to be addressed in building and managing a federated environment, that may go under the "Security", "Interoperability", "Versatility", "Automatic Selection" and "Scalability" labels. The aim of this paper is to present a survey about the approaches and challenges belonging to the "Automatic Selection" category. This work provides a literature review of different approaches adopted in the "Automatic and Optimal Cloud Service Provider Selection", also covering "Federated and Multi-Cloud" environments

An Energy-aware Brokering Algorithm to Improve Sustainability in Community Cloud

n/