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A programmable SDN+NFV-based architecture for UAV telemetry monitoring

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Publication date20/07/2017
Host publication2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)
PublisherIEEE
Pages522-527
Number of pages6
ISBN (Electronic)9781509061969
ISBN (Print)9781509061976
<mark>Original language</mark>English
EventIEEE CCNC 2017: The 14th Annual IEEE Consumer Communications & Networking Conference - Las Vegas, Las Vegas, NV, United States
Duration: 8/01/201711/01/2017
http://ccnc2017.ieee-ccnc.org/

Conference

ConferenceIEEE CCNC 2017: The 14th Annual IEEE Consumer Communications & Networking Conference
Abbreviated titleIEEE CCNC 2017
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/01/1711/01/17
Internet address

Conference

ConferenceIEEE CCNC 2017: The 14th Annual IEEE Consumer Communications & Networking Conference
Abbreviated titleIEEE CCNC 2017
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/01/1711/01/17
Internet address

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.

Bibliographic note

©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.