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Nicholas Race
Researcher at Lancaster University
Publications - 126
Citations - 1556
Nicholas Race is an academic researcher from Lancaster University. The author has contributed to research in topics: The Internet & Software-defined networking. The author has an hindex of 18, co-authored 115 publications receiving 1373 citations. Previous affiliations of Nicholas Race include University of Saskatchewan.
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Proceedings ArticleDOI
Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
TL;DR: An OpenFlow-assisted QoE Fairness Framework is proposed that aims to fairly maximise theQoE of multiple competing clients in a shared network environment by leveraging a Software Defined Networking technology, such as OpenFlow, that provides a control plane that orchestrates this functionality.
Journal ArticleDOI
Deploying Rural Community Wireless Mesh Networks
TL;DR: Lancaster University deployed a WMN in the rural village of Wray over a three-year period, providing the community with Internet service that exceeds many urban offerings.
Journal ArticleDOI
Network service orchestration standardization
Charalampos Rotsos,Daniel L. King,Arsham Farshad,Jamie Bird,Lyndon Fawcett,Nektarios Georgalas,Matthias Gunkel,Kohei Shiomoto,Aijun Wang,Andreas Mauthe,Nicholas Race,David Hutchison +11 more
TL;DR: This paper surveys existing standardization efforts for the orchestration - automation, coordination, and management - of complex set of network and function resources (both physical and virtual), and highlights the various enabling technologies, strengths and weaknesses, adoption challenges for operators, and areas where further research is required.
Proceedings ArticleDOI
Probing communities: study of a village photo display
TL;DR: A technology probe aiming to aid understanding of how digital displays can help support communities is described, deployed in a central social point in a small village and displaying user-generated photos and videos.
Proceedings ArticleDOI
OpenLIDS: a lightweight intrusion detection system for wireless mesh networks
TL;DR: This paper implements a set of lightweight anomaly detection mechanisms as part of an intrusion detection system, called OpenLIDS, and shows that even with the limited hardware resources of a mesh device, it can detect current malware behaviour in an efficient way.