RT Conference Proceedings
T1 EagleEYE: Aerial Edge-enabled Disaster Relief Response System
A1 Febrian Ardiansyah, Muhammad
A1 William, Timothy
A1 Ibrahiem Abdullaziz, Osamah
A1 Wang, Li-Chun
A1 Tien, Po-Lung
A1 Yuang, Maria C.
AB The fifth generation (5G) mobile network has paved the way for innovations across vertical industries. The integration of distributed intelligent edge into the 5G orchestrated architecture brings the benefits of low-latency and automation. A successful example of this integration is exhibited by the 5G-DIVE project, which aims at proving the technical merits and business value proposition of vertical industries such as autonomous drone surveillance and navigation. In this paper, and as part of 5G-DIVE, we present an aerial disaster relief system, called EagleEYE, which utilizes edge computing and machine learning to detect emergency situations in real-time. EagleEYE reduces training time by devising an object fusion mechanism which enables reusing existing datasets. Furthermore, EagleEYE parallelizes the detection tasks to enable real-time response. Finally, EagleEYE is evaluated in a real-world testbed and the results show that EagleEYE can reduce the inference latency by 90% with a high detection accuracy of 87%.
PB IEEE
SN 978-1-7281-4355-2
YR 2020
FD 2020-09-20
LK https://hdl.handle.net/10016/31032
UL https://hdl.handle.net/10016/31032
LA eng
NO This paper has been presented at 2020 European Conference on Networks and Communications (EuCNC).
NO This work has been partially funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881).
DS e-Archivo
RD 1 sept. 2024