Rights statement: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A programmable SDN+NFV-based architecture for UAV telemetry monitoring
AU - White, Kyle
AU - Denney, Ewen
AU - Knudson, Matt D.
AU - Marnerides, Angelos
AU - Pezaros, Dimitrios
N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2017/7/20
Y1 - 2017/7/20
N2 - 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 NetworkFunction 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 betterinsight 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 tuneand detect emerging challenges.
AB - 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 NetworkFunction 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 betterinsight 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 tuneand detect emerging challenges.
U2 - 10.1109/CCNC.2017.7983162
DO - 10.1109/CCNC.2017.7983162
M3 - Conference contribution/Paper
SN - 9781509061976
SP - 522
EP - 527
BT - 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)
PB - IEEE
T2 - IEEE CCNC 2017: The 14th Annual IEEE Consumer Communications & Networking Conference
Y2 - 8 January 2017 through 11 January 2017
ER -