The Telecommunication Networks group: main projects
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Open FirmWare for WiFi networks (2008-current)
Sponsored by... in search of a sponsor! Contact Francesco Gringoli if you want to offer your financial support to this project.
OpenFWWF, Open FirmWare for WiFi networks, is a project that wants to provide an easy and inexpensive platform to implement new Medium Access Control (MAC) mechanism, and wants to be a valid alternative to simulations and expensive ad-hoc platforms. The combination of OpenFWWF and b43 driver is a complete and cheap tool that makes testing of new MAC easy achievable.
WiSHFUL: Wireless Software and Hardware platforms for Flexible and Unified radio and network controL (2015-current)
Sponsored in part by the EU Horizon 2020 programmeWiSHFUL focuses on speeding up the development and testing cycles of wireless solution developments. It defines software modules with unified interfaces that permit wireless developers to quickly implement and validate advanced wireless network solutions. The software modules will enable the quick and efficient tuning of radio and/or network parameters to find the best configuration given the wireless device’s operating environment. The software modules will also be reprogrammable with other, new modules, which can be downloaded from app-store like repositories.
Sponsored in part by the EU 7th framework programmeThe main target of FP7-CREW is to establish an open federated test platform, which facilitates experimentally-driven research on advanced spectrum sensing, cognitive radio and cognitive networking strategies in view of horizontal and vertical spectrum sharing in licensed and unlicensed bands.
Sponsored in part by the EU 7th framework programmeWireless networks importance for the Future Internet is raising at a fast pace as mobile devices increasingly become its entry point. However, today's wireless networks are unable to rapidly adapt to evolving contexts and service needs due to their rigid architectural design. FLAVIA fosters a paradigm shift towards the Future Wireless Internet: from pre-designed link services to programmable link processors. The key concept is to expose flexible programmable interfaces enabling service customization and performance optimization through software-based exploitation of low-level operations and control primitives, e.g., transmission timing, frame customization and processing, spectrum and channel management, power control, etc.
Sponsored in part by the MiUR - MINISTERO DELL'UNIVERSITÀ E DELLA RICERCA SCIENTIFICA RESEARCH PROGRAMS (PRIN)
Network monitoring tools in use today present at least two fundamental flaws. First, their effectiveness is achieved at the price of collecting, inspecting and processing data generated by, or related to, the network users, thus infringing their right to privacy. Second, network monitoring activities do not scale, and may need to handle a huge amount of data, costly to store, transmit and process. We believe that these two flaws are intrinsic in the traditional "gather first, process later" network monitoring paradigm. IMPRESA aims at solving these issues by radically transforming traditional monolithic monitoring architectures into modular mechanisms, split into three main components. The core of the "front-end" revolves around the new concept of monitoring widgets, which are lightweight monitoring programs dynamically injected and ran directly where data is captured in real-time (a traffic probe) or stored offline (a trace repository). Each widget provides a controlled, minimized, and privacy-safe output specifically tailored to the needs of the "back-end" monitoring applications, which represent the second main component of the architecture. Finally, a new monitoring control interface will be designed, not only for managing the security aspects of each widget (e.g., by supporting widget code certification mechanisms), but also for enforcing a comprehensive authorization framework devised to control the widget's operation over the traffic data.
Sponsored in part by the EU 7th framework programme
PERIMETER's main objective is to establish a new paradigm for user-centricity in advanced networking architectures. In contrast with network-centric approaches, user-centric strategies could achieve seamless mobility driven by actual user needs rather than simply business considerations. Putting the users at the centre rather than the operator enables them to finely control the way their identity, preferences and credentials are sued. Furthermore seamless mobility is streamlined according to user preferences, enabling mobile users to be "Always Best Connected" (ABC) in multiple-access multiple-operator networks of the Future Internet.
NetSignal: statistical traffic classification (2003-2011)
Sponsored in part by Uniautomation spa
The main goal of the NetSignal project is to design and develop robust and efficient traffic classification tools for IP networks useful for anomaly detection, intrusion detection and intrusion prevention systems, enforcement of network security policies by traffic filtering, QoS management & traffic engineering and network provisioning.
New architectures for wireless access networks (2006-2011)
Sponsored by Alcatel-Lucent
The project deals with the simulation and analysis of new architectures for access networks for geographical wireless systems.
Sponsored in part by Italian Ministry for University and Research (MIUR)
Robust traffic classification, with applications to the classification of encrypted traffic.
Sponsored by Imunant srl
Novel architectures for secure mobile transactions
Protocol Oblivious Classification of Internet Traffic (2008-2010)
Sponsored in part by Cisco Systems, inc.
We believe that the problem of traffic classification cannot be solved by a single technique: the combination of different, and sometimes orthogonal, mechanisms is likely to dramatically improve the reliability and accuracy of any behavioral-based classifier. Within this project, we intend to investigate how to extend the classifications methodologies that each partner has developed and to integrate them so that a larger set of features can be considered during the flow classification. By running in parallel several classification engines, it would be possible to more accurately investigate which features to consider. State of the art supervised learning methods like Support Vector Machines or Relevance Vector Machines will be investigated to improve the accuracy of the classification engines.