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This work explores the application of collaborative virtual and augmented reality in a cloud continuum context, focusing on designing, implementing, and verifying three reference architectures for five collaborative VR/AR software deployment. The architectures designed differ in their distribution of computational load: one handles everything in the cloud, one balances the load between the cloud and the edge, and the last concentrates the load entirely on the edge. The design of the architectures was initially outlined through sequence and component diagrams and then implemented using the most appropriate technologies and frameworks. For each architecture, a specific application was developed and deployed on the various components of that architecture to test its proper functioning. Finally, the scenarios were simulated to be stressed with a significant number of users, employing tools such as Cloud Analyst to analyze performance and present well-defined and implemented reference architectures.

Internet of Things (IoT) is becoming a widespread reality, as interconnected smart devices and sensors have overtaken the IT market and invaded every aspect of the human life. This kind of development, while already foreseen by IT experts, implies additional stress to already congested networks, and may require further investments in computational power when considering centralized and Cloud based solutions. That is why a common trend is to rely on local resources, provided by smart devices themselves or by aggregators, to deal with part of the required computations: this is the base concept behind Fog Computing, which is becoming increasingly adopted as a distributed calculation solution. In this paper a methodology, initially developed within the TOREADOR European project for the distribution of Big Data computations over Cloud platforms, will be described and applied to an algorithm for the prediction of energy consumption on the basis of data coming from home sensors, already employed within the CoSSMic European Project. The objective is to demonstrate that, by applying such a methodology, it is possible to improve the calculation performances and reduce communication with centralized resources.

With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in manydifferent situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware service.

This article covers semantic methods and techniques based on semantic web technology (Berners-Lee et al., 2001) for content management in e-Learning platforms. The technique, and its prototypal implementation, involves the semantic search of learning objects (defned by David Wiley as “any digital resource that can be reused to support learning” (Wiley, 2002)) contained in SCORM (Sharable Content Objects Reference Model) “packages” (ADL) – a reference standard that ensures interoperability between different learning management systems (LMS). This technique integrates semantic techniques, combined with pattern matching techniques, to search for relevant learning objects for a given domain ontology used for queries, such as an ontology that describes the reference domain of a course of study. This correspondence is obtained through a matching operation, which returns a value of similarity between the ontology query (in OWL (Smith et al., 2004)) and learning objects such as those contained within repositories of online courses.