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Details of Grant 

EPSRC Reference: EP/L026120/1
Title: KCN: Knowledge Centric Networking
Principal Investigator: Pavlou, Professor G
Other Investigators:
Wang, Professor N Nejabati, Professor R Zervas, Professor G
Simeonidou, Professor D
Researcher Co-Investigators:
Dr M Charalambides
Project Partners:
BBC Bristol City Council Mobile VCE
Department: Electronic and Electrical Engineering
Organisation: UCL
Scheme: Standard Research
Starts: 31 December 2014 Ends: 30 June 2018 Value (£): 982,915
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Communications Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
13 May 2014 1TI3 Full Announced
Summary on Grant Application Form
The recent advent of killer applications such as content distribution, cloud computing and Internet of things (IoT), all require for the underlying network to be able to understand specific service contexts. In this project we propose the Knowledge Centric Networking (KCN) paradigm, in which knowledge is positioned at the centre of the networking landscape. The objective is to enable in-network knowledge generation and distribution in order to develop necessary network control intelligence for handling complexity and uncertainty. In order to achieve this, specific algorithms and mechanisms/protocols will be developed for knowledge acquisition, processing, dissemination and organisation both within single and across homogeneous/heterogeneous administrative domains in the Internet.

The project will investigate three styles of knowledge exchange based on Software Defined Networking (SDN) principles: Knowledge as a Tool (KaaT), Knowledge as a Service (KaaS) and Knowledge as a Cloud (KaaC). KaaT will enable intelligent network operations in dynamic network environments driven by knowledge gathered at different vantage points. We advocate a hierarchical knowledge framework in which knowledge and control functions are distributed at the right places within the network for fulfilling specific control tasks. In addition, we will invetigate knowledge sharing between different players in the Internet marketplace. This can be achieved either through explicit knowledge transfer from a knowledge provider to a knowledge consumer (KaaS), or based on open knowledge clouds where knowledge prosumers may publish or subscribe to information through an open but controlled knowledge ecosystem (KaaS).

The proposed KCN architecture will be validated through two complementary use cases. KCN-driven content traffic offloading between heterogeneous radio access technologies for the future mobile Internet aims to achieve adaptive resource control by taking into account a wide variety of knowledge associated with content, users and network conditions. In addition, KCN-driven energy management targets cross-layer energy saving techniques at both the IP and the physical optical layer according to the derived knowledge and dynamically changing context information.

The project provides direct contributions to the TI3 sub-challenges 1, 2, 3 and 4. First of all, the KCN-based knowledge ecosystem will equip the next generation Internet with necessary intelligence for handling complex requirements under dynamic conditions. Such an ecosystem, seamlessly coupled with the SDN architecture, will be able to gracefully support the ever increasing complexity and heterogeneity of future networked services and multitude of users. The two complementary use cases demonstrate how the proposed KCN framework will be instantiated in two different application domains, content traffic offloading in mobile/wireless access networks and energy efficiency in IP/optical transport networks. Use case 1 contributes to the 3rd sub-challenge, with knowledge-based content caching and traffic offloading techniques for the future content-oriented mobile Internet. Use case 2, on the other hand, contributes to the 2nd sub-challenge with intelligent energy saving mechanisms at both the IP and optical layer. Finally, with in-network knowledge inference and learning based on raw context information, the project also addresses the 4th sub-challenge of extracting understanding from data. In summary, context information captured during network/service operation will be used to derive systematic in-network knowledge and intelligence in order to deal adaptively with both complexity and uncertainty and enable near-optimal network operation.

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