SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Cloud-based Orchestration of Multimedia Services and Applications

The Mobile Cloud Networking project develops among others, several virtualized services and applications, in particular: (1) IP Multimedia Subsystem as a Service that gives the possibility to deploy a virtualized and on-demand instance of the IP Multimedia Subsystem platform, (2) Digital Signage Service as a Service that is based on a re-designed Digital Signage Service architecture, adopting the cloud computing principles, and (3) Information Centric Networking/Content Delivery Network as a Service that is used for distributing, caching and migrating content from other services. Possible designs for these virtualized services and applications have been identified and are being implemented. In particular, the architectures of the mentioned services were specified, adopting cloud computing principles, such as infrastructure sharing, elasticity, on-demand and pay-as-you-go. The benefits of Reactive Programming paradigm are presented in the context of Interactive Cloudified Digital Signage services in a Mobile Cloud Platform, as well as the benefit of interworking between different Mobile Cloud Networking Services as Digital Signage Service and Content Delivery Network Service for better performance of Video on Demand content deliver. Finally, the management of Service Level Agreements and the support of rating, charging and billing has also been considered and defined.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

Trends in incidence of oesophageal and gastric cancer according to morphology and anatomical location, in Switzerland 1982-2011.

QUESTION UNDER STUDY/PRINCIPLES This study aimed to evaluate trends in the incidence of oesophageal and gastric cancer by anatomical location and histology using nationally representative Swiss data. METHODS We included all oesophageal and gastric cancers recorded in 10 Swiss population-based cancer registries 1982-2011. We calculated age-standardised incidence rates (ASIRs) per 100 000 person-years (PY) (European standard) for both cancer sites stratified by sex, language region (German, French-Italian), morphology and anatomical location. To assess time trends, we estimated annual percentage changes (APCs) with 95% confidence intervals (95% CIs). RESULTS ASIR of oesophageal adenocarcinoma increased in both sexes and language regions (p <0.001). The steepest increase occurred in males of the German-speaking region (APC 6.8%, 95% CI 5.8-7.8) with ASIRs of 0.8 per 100,000 PY in 1982-1987 and 3.9 per 100.000 PY in 2007-2011. Incidence of oesophageal squamous cell carcinoma decreased significantly in males of both language regions by around -1.5% per year. In contrast, a slight but significant increase (APC 1.4%, 95% CI 0.3-2.4]) of oesophageal squamous cell carcinoma was observed in females of the German-speaking region. We observed stable rates for cancer of the gastric cardia. The incidence of noncardia gastric cancer decreased substantially in both sexes and language regions (p <0.001). CONCLUSION In Switzerland, the incidence of oesophageal adenocarcinoma has risen whereas incidence of noncardia gastric cancer has decreased substantially as observed in other developed countries.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.