Valeria D'Amico

Assessing 3GPP LTE-advanced as IMT-advanced technology: the WINNER+ evaluation group approach

This article describes the WINNER+ approach to performance evaluation of the 3GPP LTE-Advanced proposal as an IMT-Advanced technology candidate. The official registered WINNER+ Independent Evaluation Group evaluated this proposal against ITU-R requirements. The first part of the article gives an overview of the ITU-R evaluation process, criteria, and scenarios. The second part is focused on the working method of the evaluation group, emphasizing the simulator calibration approach. Finally, the article contains exemplary evaluation results based on analytical and simulation approaches. The obtained results allow WINNER+ to confirm that the 3GPP LTE Release 10 & Beyond (LTE-Advanced) proposal satisfies all the IMTAdvanced requirements, and thus qualifies as an IMT-advanced system.

Stack4Things as a fog computing platform for Smart City applications

Fog computing envisions computation logic to be moved at the edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed. Delegating to the Cloud the whole burden of applications could not be efficient indeed, for example in case of workload bursts. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. It enables developers and users to manage an IoT infrastructure, remotely controlling nodes as well as virtualizing their functions and creating network overlays among them, implementing a provisioning model for Cyber-Physical Systems. Moreover, it provides mechanisms to scatter the application logic on top of the involved smart objects and to choose with fine granularity which specific tasks to delegate to centralized Cloud infrastructure. In this paper, we show the core Stack4Things mechanisms implementing a Fog computing approach towards a run-time “rewireable” Smart City paradigm. We demonstrate its effectiveness in a smart mobility scenario where vehicles interact with City-level smart objects to provide end users with highly responsive geolocalised services.

An SDN/NFV telco operator platform for video broadcasting

In the last few years, SDN and NFV have introduced a new way to design, deploy, and manage networking and application services in telecommunications networks. In this context, this article presents an SDN/NFV-based platform for telco operators who intend to enter the market of video broadcasting service providers. The proposed platform allows telco operators to achieve deployment simplification and management cost reduction to support big/small live streaming content providers, and even unusual video broadcasters, in transmitting video flows to a number of interested users that can be either fixed or mobile, without the need to adopt a dedicated and expensive data delivery infrastructure. The design of the architecture proposed in this article is the result of a cooperative work realized by Telecom Italia, the University of Catania, and Wave Joint Open Lab (JOL), representing three of the most important roles in the evolution process of telecommunications networks toward network softwarization, that is, telco operator, academia, and research lab. Besides an operational and functional description of the proposed architecture, the article discusses implementation aspects of the platform, by introducing a proof of concept that has been realized on the JOLNet platform, a nationwide Italian SDN experimental network developed by Telecom Italia to interconnect its JOL labs with a large-scale geographical facility, with the aim of carrying on research and experimentations on SDN technologies and applications.

The Social Picture

We present The Social Picture, a framework to collect and explore huge amount of crowdsourced social images about public events, cultural heritage sites and other customized private events.The Social Picture aims to create social communities of users that contribute to the creation of image collections about common interests. The collections can be explored through a number of advanced Computer Vision and Machine Learning algorithms, able to capture the visual content of images in order to organize them in a semantic way. The interfaces of The Social Picture allow the users to create customized collections by exploiting semantic filters based on visual features, social network tags, geolocation, and other information related to the images.