White, K. J.S., Denney, E., Knudson, M. D., Marnerides, A. K. and Pezaros, D. P. (2017) A Programmable SDN+NFV-based Architecture for UAV Telemetry Monitoring. In: IEEE Consumer Communications and Networking Conference 2017, Las Vegas, NV, USA, 8-11 Jan 2017, pp. 522-527. ISBN 9781509061969 (doi: 10.1109/CCNC.2017.7983162)
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Abstract
The explosive growth in the worldwide use of Unmanned Aerial Vehicles (UAVs) has raised a critical concern with respect to the adequate management of their ad hoc network configuration as required by their mobility management process. As UAVs migrate among ground control stations, associated network services, routing and operational control must also rapidly migrate to ensure a seamless transition. In this paper, we present a novel, lightweight and modular architecture which supports high mobility and situational-awareness through the application of Software Defined Networking (SDN) and Network Function Virtualization (NFV) principles on top of the UAV infrastructure. By combining SDN+NFV programmability we can achieve a robust migration of UAV-related network services, such as network monitoring and anomaly detection as well as smooth UAV migration that confronts high mobility requirements. The proposed container-based monitoring and anomaly detection Network Functions (NFs) as employed within our architecture can be tuned to specific UAV types providing operators better insight during live, high-mobility deployments. We evaluate our architecture against telemetry from over 80 flights from a scientific research UAV infrastructure showing our ability to tune and detect emerging challenges.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | White, Mr Kyle and Pezaros, Professor Dimitrios |
| Authors: | White, K. J.S., Denney, E., Knudson, M. D., Marnerides, A. K., and Pezaros, D. P. |
| College/School: | College of Science and Engineering > School of Computing Science |
| ISSN: | 2331-9860 |
| ISBN: | 9781509061969 |
| Copyright Holders: | Copyright © 2017 IEEE |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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