Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

A Demonstration of Mobility Prediction as a Service in Cloudified LTE Networks

Location prediction has attracted a significant amount of research effort. Being able to predict users’ movement benefits a wide range of communication systems, including location-based service/applications, mobile access control, mobile QoS provision, and resource management for mobile computation and storage management. In this demo, we present MOBaaS, which is a cloudified Mobility and Bandwidth prediction services that can be instantiated, deployed, and disposed on-demand. Mobility prediction of MOBaaS provides location predictions of a single/group user equipments (UEs) in a future moment. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operations. We demonstrate an example of real-time mobility prediction service deployment running on OpenStack platform, and the potential benefits it bring to other invoking services.

Falls and fractures in the elderly with sinus node disease: the impact of pacemaker implantation

Background. Falls and fractures in the elderly are among the leading causes of disability. We investigated whether pacemaker implantation prevents falls in patients with SND in a large cohort of patients. Methods. Patient demographics and medical history were collected prospectively. Fall history was retrospectively reconstituted from available medical records. The 10-year probability for major osteoporotic fractures was calculated retrospectively from available medical records using the Swiss fracture risk assessment tool FRAX-Switzerland. Results. During a mean observation period of 2.3 years after implantation, the rates of fallers and injured fallers with fracture were reduced to 15% and 6%, respectively. This corresponds to a relative reduction in the number of fallers of 75% (P < 0.001) and of injured fallers of 63% (P = 0.014) after pacemaker implantation. Similarly, the number of falls was reduced from 60 (48%) before pacemaker implantation to 22 (18%) thereafter (relative reduction 63%, P = 0.035) and the number of falls with injury from 22 (18%) to 7 (6%), which corresponds to a relative reduction of 67%, P = 0.013. Conclusion. In patients with SND, pacemaker implantation significantly reduces the number of patients experiencing falls, the total number of falls, and the risk for osteoporotic fractures.

Preclinical disability as a risk factor for falls in community-dwelling older adults

BACKGROUND: Falls are common and serious problems in older adults. The goal of this study was to examine whether preclinical disability predicts incident falls in a European population of community-dwelling older adults. METHODS: Secondary data analysis was performed on a population-based longitudinal study of 1644 community-dwelling older adults living in London, U.K.; Hamburg, Germany; Solothurn, Switzerland. Data were collected at baseline and 1-year follow-up using a self-administered multidimensional health risk appraisal questionnaire, including validated questions on falls, mobility disability status (high function, preclinical disability, task difficulty), and demographic and health-related characteristics. Associations were evaluated using bivariate and multivariate logistic regression analyses. RESULTS: Overall incidence of falls was 24%, and increased by worsening mobility disability status: high function (17%), preclinical disability (32%), task difficulty (40%), test-of-trend p <.003. In multivariate analysis adjusting for other fall risk factors, preclinical disability (odds ratio [OR] = 1.7, 95% confidence interval [CI], 1.1-2.5), task difficulty (OR = 1.7, 95% CI, 1.1-2.6) and history of falls (OR = 4.7, 95% CI, 3.5-6.3) were the strongest significant predictors of falls. In stratified multivariate analyses, preclinical disability equally predicted falls in participants with (OR = 1.7, 95% CI, 1.0-3.0) and without history of falls (OR = 1.8, 95% CI, 1.1-3.0). CONCLUSIONS: This study provides longitudinal evidence that self-reported preclinical disability predicts incident falls at 1-year follow-up independent of other self-reported fall risk factors. Multidimensional geriatric assessment that includes preclinical disability may provide a unique early warning system as well as potential targets for intervention.