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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.

Papers
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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

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.