A new business model and architecture for context-aware applications provisioning in the cloud

Context-aware applications are seen as one of the killer application categories of the future, due to their ability to offer personalized services by adapting their behavior according to the users\u27 needs and changing situation. Context-aware applications rely in their operation on a complex set of functionalities (i.e. context-awareness substrates). In order to facilitate the development of novel context-aware applications and achieve efficiency in terms of resource utilization, there is a need for a unified, openly-accessible, scalable context management platform that enables the dynamic discovery, composition, and reuse of context-awareness substrates by various context-aware applications. The lack of such platform is a major impediment to the fast and resource efficient development of context aware applications. In this paper, we propose a novel virtualized context management platform in the cloud, in which a shared pool of virtualized context-awareness substrates can be offered by different providers, and leased on demand. Those substrates can be dynamically discovered and composed to enable fast and cost-effective development of a variety of context-aware applications. The proposed platform relies on a new business model which introduces the sensors substrate provider and the broker as new roles in the traditional cloud business model. A detailed software architecture and preliminary prototype implementation are also presented. © 2014 IEEE

Contextualised security operation deployment through [email protected] architecture

International audienceThe fast development of Cloud-based services and applications have a significant impact on Service Oriented Computing as it provides an efficient support to share data and processes. The de-perimeterised vision involved by these Intelligent Service Clouds lead to new security challenges: providing a consistent protection depending on the business environment conditions and on the deployment platform specific threats and vulnerabilities. To fit this context aware protection deployment challenge, we propose a [email protected] architecture, coupling Model Driven Security (MDS) and [email protected] approaches. By this way, security policies (that can be generated via a MDS process) are interpreted at runtime by a security mediator depending on the context. This proposition is illustrated thanks to a proof of concept prototype plugged on top of the FraSCAti middleware

Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges

Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this article, we present our vision of exploiting MEC for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research

Technical considerations towards mobile user QoE enhancement via Cloud interaction

This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud

Cloud-IO: Cloud Computing Platform for the Fast Deployment of Services over Wireless Sensor Networks

In the recent years, a new computing model, known as Cloud Computing, has emerged to react to the explosive growth of the number of devices connected to Internet. Cloud Computing is centered on the user and offers an efficient, secure and elastically scalable way of providing and acquiring services. Likewise, Ambient Intelligence (AmI) is also an emerging paradigm based on ubiquitous computing that proposes new ways of interaction between humans and machines, making technology adapt to the users’ necessities. One of the most important aspects in AmI is the use of context-aware technologies such as Wireless Sensor Networks (WSN) to perceive stimuli from both the users and the environment. In this regard, this paper presents Cloud-IO, a Cloud Computing platform for the fast integration and deployment of services over WSN

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study